Overview

Dataset statistics

Number of variables275
Number of observations107119
Missing cells25235723
Missing cells (%)85.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory224.7 MiB
Average record size in memory2.1 KiB

Variable types

Numeric35
Categorical152
Unsupported86
Text2

Alerts

NHCMP10A_1 has constant value ""Constant
NHCMP10A_2 has constant value ""Constant
NHCMP10A_3 has constant value ""Constant
NHCMP10A_4 has constant value ""Constant
NHCMP10A_5 has constant value ""Constant
NHCMP10AAB_1 has constant value ""Constant
NHCMP10AAB_2 has constant value ""Constant
NHCMP10AAB_3 has constant value ""Constant
NHCMP10AAB_4 has constant value ""Constant
NHCMP10ABB_1 has constant value ""Constant
NHCMP10ABB_2 has constant value ""Constant
NHCMP10ABB_3 has constant value ""Constant
NHCMP10ACB_2 has constant value ""Constant
NHCMP10ACB_3 has constant value ""Constant
NHCMP10ACB_4 has constant value ""Constant
NHCMP10ADB_1 has constant value ""Constant
NHCMP10ADB_3 has constant value ""Constant
NHCMP10ADB_4 has constant value ""Constant
NHCMP10AEB_2 has constant value ""Constant
NHCMP10AEB_3 has constant value ""Constant
NHCMP10AEB_4 has constant value ""Constant
NHCMP12A_1 has constant value ""Constant
NHCMP12A_2 has constant value ""Constant
NHCMP12A_3 has constant value ""Constant
NHCMP12A_4 has constant value ""Constant
NHCMP12A_5 has constant value ""Constant
NHCMP12A_6 has constant value ""Constant
NHCMP12A_7 has constant value ""Constant
NHCMP12A_8 has constant value ""Constant
NHCMP12A_9 has constant value ""Constant
NHCMP12A_10 has constant value ""Constant
NHCMP12A_11 has constant value ""Constant
NHCMP12A_12 has constant value ""Constant
NHCMP12A_13 has constant value ""Constant
NHCMP12A_14 has constant value ""Constant
NHCMP12A_15 has constant value ""Constant
NHCMP12A_16 has constant value ""Constant
NHCMP12A_17 has constant value ""Constant
NHCMP12A_18 has constant value ""Constant
NHCMP12A_19 has constant value ""Constant
NHCMP12A_20 has constant value ""Constant
NHCMP12A_21 has constant value ""Constant
NHCMP12A_22 has constant value ""Constant
NHCMP12A_23 has constant value ""Constant
NHCMP12A_24 has constant value ""Constant
NHCMP12AAB_1 has constant value ""Constant
NHCMP12AAB_2 has constant value ""Constant
NHCMP12AAB_3 has constant value ""Constant
NHCMP12AAB_4 has constant value ""Constant
NHCMP12ABB_1 has constant value ""Constant
NHCMP12ABB_2 has constant value ""Constant
NHCMP12ABB_3 has constant value ""Constant
NHCMP12ABB_4 has constant value ""Constant
NHCMP12ACB_3 has constant value ""Constant
NHCMP12ACB_4 has constant value ""Constant
NHCMP12ADB_1 has constant value ""Constant
NHCMP12ADB_2 has constant value ""Constant
NHCMP12ADB_3 has constant value ""Constant
NHCMP12ADB_4 has constant value ""Constant
NHCMP12AEB_2 has constant value ""Constant
NHCMP12AEB_3 has constant value ""Constant
NHCMP12AFB_2 has constant value ""Constant
NHCMP12AFB_3 has constant value ""Constant
NHCMP12AFB_4 has constant value ""Constant
NHCMP12AGB_1 has constant value ""Constant
NHCMP12AGB_2 has constant value ""Constant
NHCMP12AGB_3 has constant value ""Constant
NHCMP12AHB_2 has constant value ""Constant
NHCMP12AHB_3 has constant value ""Constant
NHCMP12AIB_1 has constant value ""Constant
NHCMP12AIB_2 has constant value ""Constant
NHCMP12AIB_3 has constant value ""Constant
NHCMP12AIB_4 has constant value ""Constant
NHCMP12AJB_2 has constant value ""Constant
NHCMP12AJB_3 has constant value ""Constant
NHCMP12AJB_4 has constant value ""Constant
NHCMP12AKB_1 has constant value ""Constant
NHCMP12AKB_2 has constant value ""Constant
NHCMP12AKB_3 has constant value ""Constant
NHCMPA12ALB_1 has constant value ""Constant
NHCMPA12ALB_2 has constant value ""Constant
NHCMPA12ALB_3 has constant value ""Constant
NHCMPA12ALB_4 has constant value ""Constant
NHCMP12ALB_1 has constant value ""Constant
NHCMP12ALB_2 has constant value ""Constant
NHCMP12ALB_3 has constant value ""Constant
NHCMP12ALB_4 has constant value ""Constant
NHCMP12ANB_2 has constant value ""Constant
NHCMP12ANB_3 has constant value ""Constant
NHCMP12ANB_4 has constant value ""Constant
NHCMP12AOB_1 has constant value ""Constant
NHCMP12AOB_2 has constant value ""Constant
NHCMP12AOB_3 has constant value ""Constant
NHCMP12AOB_4 has constant value ""Constant
NHCMP12APB_1 has constant value ""Constant
NHCMP12APB_2 has constant value ""Constant
NHCMP12APB_3 has constant value ""Constant
NHCMP12APB_4 has constant value ""Constant
NHCMP12AQB_2 has constant value ""Constant
NHCMP12AQB_3 has constant value ""Constant
NHCMP12ARB_2 has constant value ""Constant
NHCMP12ARB_3 has constant value ""Constant
NHCMP12ARB_4 has constant value ""Constant
NHCMPA12ARB_2 has constant value ""Constant
NHCMPA12ARB_3 has constant value ""Constant
NHCMP12ASB_2 has constant value ""Constant
NHCMP12ASB_3 has constant value ""Constant
NHCMP12ASB_4 has constant value ""Constant
NHCMP12ATB_3 has constant value ""Constant
NHCMP12AUB_1 has constant value ""Constant
NHCMP12AUB_3 has constant value ""Constant
NHCMP12AUB_4 has constant value ""Constant
NHCMP12AUA22 has constant value ""Constant
NHCMP12AUB22_3 has constant value ""Constant
NHCMP12AUB23_1 has constant value ""Constant
NHCMP12AUB23_2 has constant value ""Constant
NHCMP12AUB23_3 has constant value ""Constant
DIRECTORIO is highly overall correlated with DIRECTORIO_HOG and 3 other fieldsHigh correlation
DIRECTORIO_HOG is highly overall correlated with DIRECTORIO and 3 other fieldsHigh correlation
NHCMP10 is highly overall correlated with NHCMP10ADA and 2 other fieldsHigh correlation
NHCMP10AAA is highly overall correlated with NHCMP10ABA and 15 other fieldsHigh correlation
NHCMP10ABA is highly overall correlated with NHCMP10AAA and 25 other fieldsHigh correlation
NHCMP10ACA is highly overall correlated with NHCMP10AAA and 11 other fieldsHigh correlation
NHCMP10ADA is highly overall correlated with NHCMP10 and 29 other fieldsHigh correlation
NHCMP10AEA is highly overall correlated with NHCMP10AAA and 11 other fieldsHigh correlation
NHCMP11A is highly overall correlated with NHCMP10ADA and 4 other fieldsHigh correlation
NHCMP11B is highly overall correlated with NHCMP10ABA and 5 other fieldsHigh correlation
NHCMP11C is highly overall correlated with NHCMP10ABA and 4 other fieldsHigh correlation
NHCMP11D is highly overall correlated with NHCMP10ABA and 4 other fieldsHigh correlation
NHCMP11E is highly overall correlated with NHCMP10ADA and 4 other fieldsHigh correlation
NHCMP11F is highly overall correlated with NHCMP10ABA and 6 other fieldsHigh correlation
NHCMP11G is highly overall correlated with NHCMP10ABA and 3 other fieldsHigh correlation
NHCMP11H is highly overall correlated with NHCMP10ADA and 2 other fieldsHigh correlation
NHCMP11HB is highly overall correlated with NHCMP11NUB and 5 other fieldsHigh correlation
NHCMP11I is highly overall correlated with NHCMP10ABA and 4 other fieldsHigh correlation
NHCMP11J is highly overall correlated with NHCMP10ADA and 3 other fieldsHigh correlation
NHCMP11K is highly overall correlated with NHCMP10ABA and 5 other fieldsHigh correlation
NHCMP11L is highly overall correlated with NHCMP10ADA and 3 other fieldsHigh correlation
NHCMP11N is highly overall correlated with NHCMP10ABA and 4 other fieldsHigh correlation
NHCMP11NU is highly overall correlated with NHCMP10AAA and 7 other fieldsHigh correlation
NHCMP11NUB is highly overall correlated with NHCMP10AAA and 2 other fieldsHigh correlation
NHCMP11O is highly overall correlated with NHCMP10ABA and 4 other fieldsHigh correlation
NHCMP11P is highly overall correlated with NHCMP10ADA and 2 other fieldsHigh correlation
NHCMP11Q is highly overall correlated with NHCMP10ADA and 2 other fieldsHigh correlation
NHCMP11R is highly overall correlated with NHCMP10ABA and 4 other fieldsHigh correlation
NHCMP11S is highly overall correlated with NHCMP10ABA and 4 other fieldsHigh correlation
NHCMP11SB is highly overall correlated with NHCMP11HB and 3 other fieldsHigh correlation
NHCMP11T is highly overall correlated with NHCMP10ABA and 5 other fieldsHigh correlation
NHCMP11U is highly overall correlated with NHCMP10ABA and 3 other fieldsHigh correlation
NHCMP12 is highly overall correlated with NHCMP12ATA and 1 other fieldsHigh correlation
NHCMP12AAA is highly overall correlated with NHCMP10ABA and 20 other fieldsHigh correlation
NHCMP12ABA is highly overall correlated with NHCMP10ABA and 16 other fieldsHigh correlation
NHCMP12ACA is highly overall correlated with NHCMP10AAA and 12 other fieldsHigh correlation
NHCMP12ADA is highly overall correlated with NHCMP10AAA and 12 other fieldsHigh correlation
NHCMP12AFA is highly overall correlated with DIRECTORIO and 17 other fieldsHigh correlation
NHCMP12AGA is highly overall correlated with NHCMP11A and 13 other fieldsHigh correlation
NHCMP12AHA is highly overall correlated with NHCMP10ABA and 8 other fieldsHigh correlation
NHCMP12AIA is highly overall correlated with NHCMP10ACA and 15 other fieldsHigh correlation
NHCMP12AJA is highly overall correlated with NHCMP10AAA and 19 other fieldsHigh correlation
NHCMP12ALA is highly overall correlated with NHCMP10AAA and 13 other fieldsHigh correlation
NHCMP12ANA is highly overall correlated with NHCMP12AAA and 8 other fieldsHigh correlation
NHCMP12AOA is highly overall correlated with NHCMP12AAA and 10 other fieldsHigh correlation
NHCMP12APA is highly overall correlated with NHCMP12AAA and 10 other fieldsHigh correlation
NHCMP12AQA is highly overall correlated with NHCMP10AAA and 12 other fieldsHigh correlation
NHCMP12ARA is highly overall correlated with NHCMP12AAA and 8 other fieldsHigh correlation
NHCMP12ASA is highly overall correlated with DIRECTORIO and 11 other fieldsHigh correlation
NHCMP12ATA is highly overall correlated with DIRECTORIO and 35 other fieldsHigh correlation
NHCMP12AUA23 is highly overall correlated with NHCMP10 and 30 other fieldsHigh correlation
NHCMP9A is highly overall correlated with NHCMP10ABA and 3 other fieldsHigh correlation
NHCMP9AB is highly overall correlated with NHCMP10AEA and 2 other fieldsHigh correlation
NHCMP9B is highly overall correlated with NHCMP10ABA and 14 other fieldsHigh correlation
NHCMP9BA is highly overall correlated with NHCMP12AAA and 6 other fieldsHigh correlation
NHCMP9BB is highly overall correlated with NHCMP11HB and 2 other fieldsHigh correlation
NHCMP9C is highly overall correlated with NHCMP10ABA and 8 other fieldsHigh correlation
NHCMP9CB is highly overall correlated with NHCMP11SB and 5 other fieldsHigh correlation
NHCMP9D is highly overall correlated with NHCMP10ABA and 13 other fieldsHigh correlation
NHCMP9DA is highly overall correlated with NHCMP12ACA and 4 other fieldsHigh correlation
NHCMP9DB is highly overall correlated with NHCMP10AAA and 1 other fieldsHigh correlation
NHCMP9E is highly overall correlated with NHCMP10ABA and 8 other fieldsHigh correlation
NHCMPA11AL is highly overall correlated with NHCMP12AFA and 1 other fieldsHigh correlation
NHCMPA11R is highly overall correlated with NHCMP10ABA and 4 other fieldsHigh correlation
NHCMPA11U24 is highly overall correlated with NHCMP10ABA and 20 other fieldsHigh correlation
NHCMPA11UB24 is highly overall correlated with NHCMP11HB and 1 other fieldsHigh correlation
NHCMPA12ALA is highly overall correlated with NHCMP10AAA and 15 other fieldsHigh correlation
NHCMPA12ARA is highly overall correlated with NHCMP12AAA and 8 other fieldsHigh correlation
NHCMPB11U25 is highly overall correlated with NHCMP10AAA and 23 other fieldsHigh correlation
NHCMP9B is highly imbalanced (84.1%)Imbalance
NHCMP9C is highly imbalanced (68.9%)Imbalance
NHCMP9D is highly imbalanced (93.1%)Imbalance
NHCMP10 is highly imbalanced (96.2%)Imbalance
NHCMP11A is highly imbalanced (63.6%)Imbalance
NHCMP11B is highly imbalanced (62.6%)Imbalance
NHCMP11C is highly imbalanced (73.1%)Imbalance
NHCMP11D is highly imbalanced (72.4%)Imbalance
NHCMP11E is highly imbalanced (85.9%)Imbalance
NHCMP11F is highly imbalanced (91.7%)Imbalance
NHCMP11G is highly imbalanced (55.4%)Imbalance
NHCMP11H is highly imbalanced (75.4%)Imbalance
NHCMP11I is highly imbalanced (69.1%)Imbalance
NHCMP11J is highly imbalanced (71.5%)Imbalance
NHCMP11K is highly imbalanced (62.9%)Imbalance
NHCMP11N is highly imbalanced (71.7%)Imbalance
NHCMP11O is highly imbalanced (69.9%)Imbalance
NHCMP11Q is highly imbalanced (80.0%)Imbalance
NHCMP11R is highly imbalanced (57.4%)Imbalance
NHCMP11S is highly imbalanced (95.7%)Imbalance
NHCMP11T is highly imbalanced (85.1%)Imbalance
NHCMP11U is highly imbalanced (64.7%)Imbalance
NHCMP11NU is highly imbalanced (85.4%)Imbalance
NHCMPA11U24 is highly imbalanced (81.3%)Imbalance
NHCMPB11U25 is highly imbalanced (88.8%)Imbalance
NHCMP12 is highly imbalanced (93.6%)Imbalance
NHCMP9AA has 84515 (78.9%) missing valuesMissing
NHCMP9AB has 84515 (78.9%) missing valuesMissing
NHCMP9BA has 104633 (97.7%) missing valuesMissing
NHCMP9BB has 104633 (97.7%) missing valuesMissing
NHCMP9CA has 101136 (94.4%) missing valuesMissing
NHCMP9CB has 101136 (94.4%) missing valuesMissing
NHCMP9DA has 106232 (99.2%) missing valuesMissing
NHCMP9DB has 106232 (99.2%) missing valuesMissing
NHCMP9EA has 85584 (79.9%) missing valuesMissing
NHCMP10A_1 has 106735 (99.6%) missing valuesMissing
NHCMP10A_2 has 107102 (> 99.9%) missing valuesMissing
NHCMP10A_3 has 107086 (> 99.9%) missing valuesMissing
NHCMP10A_4 has 107109 (> 99.9%) missing valuesMissing
NHCMP10A_5 has 107106 (> 99.9%) missing valuesMissing
NHCMP10AAA has 106735 (99.6%) missing valuesMissing
NHCMP10AAB_1 has 107114 (> 99.9%) missing valuesMissing
NHCMP10AAB_2 has 107108 (> 99.9%) missing valuesMissing
NHCMP10AAB_3 has 106745 (99.7%) missing valuesMissing
NHCMP10AAB_4 has 107117 (> 99.9%) missing valuesMissing
NHCMP10ABA has 107102 (> 99.9%) missing valuesMissing
NHCMP10ABB_1 has 107118 (> 99.9%) missing valuesMissing
NHCMP10ABB_2 has 107118 (> 99.9%) missing valuesMissing
NHCMP10ABB_3 has 107104 (> 99.9%) missing valuesMissing
NHCMP10ABB_4 has 107119 (100.0%) missing valuesMissing
NHCMP10ACA has 107086 (> 99.9%) missing valuesMissing
NHCMP10ACB_1 has 107119 (100.0%) missing valuesMissing
NHCMP10ACB_2 has 107118 (> 99.9%) missing valuesMissing
NHCMP10ACB_3 has 107087 (> 99.9%) missing valuesMissing
NHCMP10ACB_4 has 107118 (> 99.9%) missing valuesMissing
NHCMP10ADA has 107109 (> 99.9%) missing valuesMissing
NHCMP10ADB_1 has 107118 (> 99.9%) missing valuesMissing
NHCMP10ADB_2 has 107119 (100.0%) missing valuesMissing
NHCMP10ADB_3 has 107111 (> 99.9%) missing valuesMissing
NHCMP10ADB_4 has 107118 (> 99.9%) missing valuesMissing
NHCMP10AEA has 107106 (> 99.9%) missing valuesMissing
NHCMP10AEB_1 has 107119 (100.0%) missing valuesMissing
NHCMP10AEB_2 has 107114 (> 99.9%) missing valuesMissing
NHCMP10AEB_3 has 107112 (> 99.9%) missing valuesMissing
NHCMP10AEB_4 has 107118 (> 99.9%) missing valuesMissing
NHCMP11AA has 99671 (93.0%) missing valuesMissing
NHCMP11AB has 99671 (93.0%) missing valuesMissing
NHCMP11BA has 99382 (92.8%) missing valuesMissing
NHCMP11BB has 99382 (92.8%) missing valuesMissing
NHCMP11CA has 102190 (95.4%) missing valuesMissing
NHCMP11CB has 102190 (95.4%) missing valuesMissing
NHCMP11DA has 102016 (95.2%) missing valuesMissing
NHCMP11DB has 102016 (95.2%) missing valuesMissing
NHCMP11EA has 104989 (98.0%) missing valuesMissing
NHCMP11EB has 104989 (98.0%) missing valuesMissing
NHCMP11FA has 106012 (99.0%) missing valuesMissing
NHCMP11FB has 106012 (99.0%) missing valuesMissing
NHCMP11GA has 97155 (90.7%) missing valuesMissing
NHCMP11GB has 97155 (90.7%) missing valuesMissing
NHCMP11HA has 102741 (95.9%) missing valuesMissing
NHCMP11HB has 102741 (95.9%) missing valuesMissing
NHCMP11IA has 101184 (94.5%) missing valuesMissing
NHCMP11IB has 101184 (94.5%) missing valuesMissing
NHCMP11JA has 101802 (95.0%) missing valuesMissing
NHCMP11JB has 101802 (95.0%) missing valuesMissing
NHCMP11KA has 99480 (92.9%) missing valuesMissing
NHCMP11KB has 99480 (92.9%) missing valuesMissing
NHCMPA11ALA has 68057 (63.5%) missing valuesMissing
NHCMPA11ALB has 68057 (63.5%) missing valuesMissing
NHCMP11LA has 81181 (75.8%) missing valuesMissing
NHCMP11LB has 81181 (75.8%) missing valuesMissing
NHCMP11NA has 101848 (95.1%) missing valuesMissing
NHCMP11NB has 101848 (95.1%) missing valuesMissing
NHCMP11OA has 101394 (94.7%) missing valuesMissing
NHCMP11OB has 101394 (94.7%) missing valuesMissing
NHCMP11PA has 73013 (68.2%) missing valuesMissing
NHCMP11PB has 73013 (68.2%) missing valuesMissing
NHCMP11QA has 103778 (96.9%) missing valuesMissing
NHCMP11QB has 103778 (96.9%) missing valuesMissing
NHCMP11RA has 97824 (91.3%) missing valuesMissing
NHCMP11RB has 97824 (91.3%) missing valuesMissing
NHCMPA11RA has 82853 (77.3%) missing valuesMissing
NHCMPA11RB has 82853 (77.3%) missing valuesMissing
NHCMP11SA has 106619 (99.5%) missing valuesMissing
NHCMP11SB has 106619 (99.5%) missing valuesMissing
NHCMP11TA has 104827 (97.9%) missing valuesMissing
NHCMP11TB has 104827 (97.9%) missing valuesMissing
NHCMP11UA has 99981 (93.3%) missing valuesMissing
NHCMP11UB has 99981 (93.3%) missing valuesMissing
NHCMP11NUA has 104893 (97.9%) missing valuesMissing
NHCMP11NUB has 104893 (97.9%) missing valuesMissing
NHCMPA11U24 has 84274 (78.7%) missing valuesMissing
NHCMPA11UA24 has 106467 (99.4%) missing valuesMissing
NHCMPA11UB24 has 106467 (99.4%) missing valuesMissing
NHCMPB11U25 has 84274 (78.7%) missing valuesMissing
NHCMPB11UA25 has 106776 (99.7%) missing valuesMissing
NHCMPB11UB25 has 106776 (99.7%) missing valuesMissing
NHCMP12A_1 has 106989 (99.9%) missing valuesMissing
NHCMP12A_2 has 106964 (99.9%) missing valuesMissing
NHCMP12A_3 has 107103 (> 99.9%) missing valuesMissing
NHCMP12A_4 has 107084 (> 99.9%) missing valuesMissing
NHCMP12A_5 has 107108 (> 99.9%) missing valuesMissing
NHCMP12A_6 has 107112 (> 99.9%) missing valuesMissing
NHCMP12A_7 has 107109 (> 99.9%) missing valuesMissing
NHCMP12A_8 has 107055 (99.9%) missing valuesMissing
NHCMP12A_9 has 107023 (99.9%) missing valuesMissing
NHCMP12A_10 has 107016 (99.9%) missing valuesMissing
NHCMP12A_11 has 107055 (99.9%) missing valuesMissing
NHCMP12A_12 has 106999 (99.9%) missing valuesMissing
NHCMP12A_13 has 107084 (> 99.9%) missing valuesMissing
NHCMP12A_14 has 107110 (> 99.9%) missing valuesMissing
NHCMP12A_15 has 107075 (> 99.9%) missing valuesMissing
NHCMP12A_16 has 107058 (99.9%) missing valuesMissing
NHCMP12A_17 has 106982 (99.9%) missing valuesMissing
NHCMP12A_18 has 107093 (> 99.9%) missing valuesMissing
NHCMP12A_19 has 107075 (> 99.9%) missing valuesMissing
NHCMP12A_20 has 107095 (> 99.9%) missing valuesMissing
NHCMP12A_21 has 107107 (> 99.9%) missing valuesMissing
NHCMP12A_22 has 107114 (> 99.9%) missing valuesMissing
NHCMP12A_23 has 107084 (> 99.9%) missing valuesMissing
NHCMP12A_24 has 107118 (> 99.9%) missing valuesMissing
NHCMP12A_25 has 107119 (100.0%) missing valuesMissing
NHCMP12AAA has 106989 (99.9%) missing valuesMissing
NHCMP12AAB_1 has 107117 (> 99.9%) missing valuesMissing
NHCMP12AAB_2 has 107114 (> 99.9%) missing valuesMissing
NHCMP12AAB_3 has 106998 (99.9%) missing valuesMissing
NHCMP12AAB_4 has 107114 (> 99.9%) missing valuesMissing
NHCMP12ABA has 106964 (99.9%) missing valuesMissing
NHCMP12ABB_1 has 107118 (> 99.9%) missing valuesMissing
NHCMP12ABB_2 has 107113 (> 99.9%) missing valuesMissing
NHCMP12ABB_3 has 106972 (99.9%) missing valuesMissing
NHCMP12ABB_4 has 107116 (> 99.9%) missing valuesMissing
NHCMP12ACA has 107103 (> 99.9%) missing valuesMissing
NHCMP12ACB_1 has 107119 (100.0%) missing valuesMissing
NHCMP12ACB_2 has 107119 (100.0%) missing valuesMissing
NHCMP12ACB_3 has 107104 (> 99.9%) missing valuesMissing
NHCMP12ACB_4 has 107118 (> 99.9%) missing valuesMissing
NHCMP12ADA has 107084 (> 99.9%) missing valuesMissing
NHCMP12ADB_1 has 107117 (> 99.9%) missing valuesMissing
NHCMP12ADB_2 has 107117 (> 99.9%) missing valuesMissing
NHCMP12ADB_3 has 107088 (> 99.9%) missing valuesMissing
NHCMP12ADB_4 has 107118 (> 99.9%) missing valuesMissing
NHCMP12AEA has 107108 (> 99.9%) missing valuesMissing
NHCMP12AEB_1 has 107119 (100.0%) missing valuesMissing
NHCMP12AEB_2 has 107118 (> 99.9%) missing valuesMissing
NHCMP12AEB_3 has 107109 (> 99.9%) missing valuesMissing
NHCMP12AEB_4 has 107119 (100.0%) missing valuesMissing
NHCMP12AFA has 107112 (> 99.9%) missing valuesMissing
NHCMP12AFB_1 has 107119 (100.0%) missing valuesMissing
NHCMP12AFB_2 has 107118 (> 99.9%) missing valuesMissing
NHCMP12AFB_3 has 107113 (> 99.9%) missing valuesMissing
NHCMP12AFB_4 has 107117 (> 99.9%) missing valuesMissing
NHCMP12AGA has 107109 (> 99.9%) missing valuesMissing
NHCMP12AGB_1 has 107117 (> 99.9%) missing valuesMissing
NHCMP12AGB_2 has 107118 (> 99.9%) missing valuesMissing
NHCMP12AGB_3 has 107112 (> 99.9%) missing valuesMissing
NHCMP12AGB_4 has 107119 (100.0%) missing valuesMissing
NHCMP12AHA has 107055 (99.9%) missing valuesMissing
NHCMP12AHB_1 has 107119 (100.0%) missing valuesMissing
NHCMP12AHB_2 has 107118 (> 99.9%) missing valuesMissing
NHCMP12AHB_3 has 107056 (99.9%) missing valuesMissing
NHCMP12AHB_4 has 107119 (100.0%) missing valuesMissing
NHCMP12AIA has 107023 (99.9%) missing valuesMissing
NHCMP12AIB_1 has 107118 (> 99.9%) missing valuesMissing
NHCMP12AIB_2 has 107117 (> 99.9%) missing valuesMissing
NHCMP12AIB_3 has 107025 (99.9%) missing valuesMissing
NHCMP12AIB_4 has 107118 (> 99.9%) missing valuesMissing
NHCMP12AJA has 107016 (99.9%) missing valuesMissing
NHCMP12AJB_1 has 107119 (100.0%) missing valuesMissing
NHCMP12AJB_2 has 107114 (> 99.9%) missing valuesMissing
NHCMP12AJB_3 has 107020 (99.9%) missing valuesMissing
NHCMP12AJB_4 has 107118 (> 99.9%) missing valuesMissing
NHCMP12AKA has 107055 (99.9%) missing valuesMissing
NHCMP12AKB_1 has 107118 (> 99.9%) missing valuesMissing
NHCMP12AKB_2 has 107118 (> 99.9%) missing valuesMissing
NHCMP12AKB_3 has 107057 (99.9%) missing valuesMissing
NHCMP12AKB_4 has 107119 (100.0%) missing valuesMissing
NHCMPA12ALA has 106999 (99.9%) missing valuesMissing
NHCMPA12ALB_1 has 107118 (> 99.9%) missing valuesMissing
NHCMPA12ALB_2 has 107118 (> 99.9%) missing valuesMissing
NHCMPA12ALB_3 has 107001 (99.9%) missing valuesMissing
NHCMPA12ALB_4 has 107117 (> 99.9%) missing valuesMissing
NHCMP12ALA has 107084 (> 99.9%) missing valuesMissing
NHCMP12ALB_1 has 107116 (> 99.9%) missing valuesMissing
NHCMP12ALB_2 has 107116 (> 99.9%) missing valuesMissing
NHCMP12ALB_3 has 107091 (> 99.9%) missing valuesMissing
NHCMP12ALB_4 has 107118 (> 99.9%) missing valuesMissing
NHCMP12ANA has 107075 (> 99.9%) missing valuesMissing
NHCMP12ANB_1 has 107119 (100.0%) missing valuesMissing
NHCMP12ANB_2 has 107116 (> 99.9%) missing valuesMissing
NHCMP12ANB_3 has 107079 (> 99.9%) missing valuesMissing
NHCMP12ANB_4 has 107118 (> 99.9%) missing valuesMissing
NHCMP12AOA has 107058 (99.9%) missing valuesMissing
NHCMP12AOB_1 has 107118 (> 99.9%) missing valuesMissing
NHCMP12AOB_2 has 107116 (> 99.9%) missing valuesMissing
NHCMP12AOB_3 has 107063 (99.9%) missing valuesMissing
NHCMP12AOB_4 has 107118 (> 99.9%) missing valuesMissing
NHCMP12APA has 106982 (99.9%) missing valuesMissing
NHCMP12APB_1 has 107111 (> 99.9%) missing valuesMissing
NHCMP12APB_2 has 107105 (> 99.9%) missing valuesMissing
NHCMP12APB_3 has 107004 (99.9%) missing valuesMissing
NHCMP12APB_4 has 107118 (> 99.9%) missing valuesMissing
NHCMP12AQA has 107093 (> 99.9%) missing valuesMissing
NHCMP12AQB_1 has 107119 (100.0%) missing valuesMissing
NHCMP12AQB_2 has 107118 (> 99.9%) missing valuesMissing
NHCMP12AQB_3 has 107094 (> 99.9%) missing valuesMissing
NHCMP12AQB_4 has 107119 (100.0%) missing valuesMissing
NHCMP12ARA has 107075 (> 99.9%) missing valuesMissing
NHCMP12ARB_1 has 107119 (100.0%) missing valuesMissing
NHCMP12ARB_2 has 107113 (> 99.9%) missing valuesMissing
NHCMP12ARB_3 has 107082 (> 99.9%) missing valuesMissing
NHCMP12ARB_4 has 107118 (> 99.9%) missing valuesMissing
NHCMPA12ARA has 107095 (> 99.9%) missing valuesMissing
NHCMPA12ARB_1 has 107119 (100.0%) missing valuesMissing
NHCMPA12ARB_2 has 107117 (> 99.9%) missing valuesMissing
NHCMPA12ARB_3 has 107097 (> 99.9%) missing valuesMissing
NHCMPA12ARB_4 has 107119 (100.0%) missing valuesMissing
NHCMP12ASA has 107107 (> 99.9%) missing valuesMissing
NHCMP12ASB_1 has 107119 (100.0%) missing valuesMissing
NHCMP12ASB_2 has 107118 (> 99.9%) missing valuesMissing
NHCMP12ASB_3 has 107109 (> 99.9%) missing valuesMissing
NHCMP12ASB_4 has 107118 (> 99.9%) missing valuesMissing
NHCMP12ATA has 107114 (> 99.9%) missing valuesMissing
NHCMP12ATB_1 has 107119 (100.0%) missing valuesMissing
NHCMP12ATB_2 has 107119 (100.0%) missing valuesMissing
NHCMP12ATB_3 has 107114 (> 99.9%) missing valuesMissing
NHCMP12ATB_4 has 107119 (100.0%) missing valuesMissing
NHCMP12AUA has 107084 (> 99.9%) missing valuesMissing
NHCMP12AUB_1 has 107118 (> 99.9%) missing valuesMissing
NHCMP12AUB_2 has 107119 (100.0%) missing valuesMissing
NHCMP12AUB_3 has 107089 (> 99.9%) missing valuesMissing
NHCMP12AUB_4 has 107115 (> 99.9%) missing valuesMissing
NHCMP12AUA22 has 107118 (> 99.9%) missing valuesMissing
NHCMP12AUB22_1 has 107119 (100.0%) missing valuesMissing
NHCMP12AUB22_2 has 107119 (100.0%) missing valuesMissing
NHCMP12AUB22_3 has 107118 (> 99.9%) missing valuesMissing
NHCMP12AUB22_4 has 107119 (100.0%) missing valuesMissing
NHCMP12AUA23 has 107110 (> 99.9%) missing valuesMissing
NHCMP12AUB23_1 has 107118 (> 99.9%) missing valuesMissing
NHCMP12AUB23_2 has 107116 (> 99.9%) missing valuesMissing
NHCMP12AUB23_3 has 107114 (> 99.9%) missing valuesMissing
NHCMP12AUB23_4 has 107119 (100.0%) missing valuesMissing
NHCMP12AUA24 has 107119 (100.0%) missing valuesMissing
NHCMP12AUB24_1 has 107119 (100.0%) missing valuesMissing
NHCMP12AUB24_2 has 107119 (100.0%) missing valuesMissing
NHCMP12AUB24_3 has 107119 (100.0%) missing valuesMissing
NHCMP12AUB24_4 has 107119 (100.0%) missing valuesMissing
NHCMP9BB is highly skewed (γ1 = 24.4810935)Skewed
NHCMP9CB is highly skewed (γ1 = 25.09788444)Skewed
NHCMP9DA is highly skewed (γ1 = 22.17628033)Skewed
NHCMP12ATA is uniformly distributedUniform
DIRECTORIO_HOG has unique valuesUnique
NHCMP9AA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP9CA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP9EA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP10ABB_4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP10ACB_1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP10ADB_2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP10AEB_1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11AA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11AB is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11BA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11BB is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11CA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11CB is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11DA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11DB is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11EA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11FA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11FB is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11GA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11GB is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11HA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11IA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11IB is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11JA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11JB is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11KA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11KB is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMPA11ALA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMPA11ALB is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11LA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11LB is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11NA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11NB is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11OA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11OB is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11PA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11PB is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11QA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11QB is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11RA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11RB is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMPA11RA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMPA11RB is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11SA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11TA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11TB is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11UA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11UB is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP11NUA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMPA11UA24 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMPB11UA25 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMPB11UB25 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12A_25 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12ACB_1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12ACB_2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12AEA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12AEB_1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12AEB_4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12AFB_1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12AGB_4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12AHB_1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12AHB_4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12AJB_1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12AKA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12AKB_4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12ANB_1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12AQB_1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12AQB_4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12ARB_1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMPA12ARB_1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMPA12ARB_4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12ASB_1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12ATB_1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12ATB_2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12ATB_4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12AUA is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12AUB_2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12AUB22_1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12AUB22_2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12AUB22_4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12AUB23_4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12AUA24 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12AUB24_1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12AUB24_2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12AUB24_3 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP12AUB24_4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
NHCMP9AB has 20574 (19.2%) zerosZeros
NHCMP9BB has 2376 (2.2%) zerosZeros
NHCMP9CB has 5612 (5.2%) zerosZeros
NHCMP11HB has 4085 (3.8%) zerosZeros
NHCMP11NUB has 2003 (1.9%) zerosZeros

Reproduction

Analysis started2024-04-22 02:42:21.226193
Analysis finished2024-04-22 02:45:52.848205
Duration3 minutes and 31.62 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

DIRECTORIO
Real number (ℝ)

HIGH CORRELATION 

Distinct106467
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1116653.9
Minimum166238
Maximum3006812
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:45:52.944868image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum166238
5-th percentile330848.9
Q1998809.5
median1053492
Q31151058.5
95-th percentile3000687.1
Maximum3006812
Range2840574
Interquartile range (IQR)152249

Descriptive statistics

Standard deviation598061.79
Coefficient of variation (CV)0.53558384
Kurtosis3.3293812
Mean1116653.9
Median Absolute Deviation (MAD)83904
Skewness1.576843
Sum1.1961485 × 1011
Variance3.5767791 × 1011
MonotonicityNot monotonic
2024-04-21T21:45:53.089733image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
331798 6
 
< 0.1%
1065059 6
 
< 0.1%
1138927 6
 
< 0.1%
328719 5
 
< 0.1%
1160292 5
 
< 0.1%
1166213 5
 
< 0.1%
342432 5
 
< 0.1%
990174 5
 
< 0.1%
1165332 4
 
< 0.1%
1016627 4
 
< 0.1%
Other values (106457) 107068
> 99.9%
ValueCountFrequency (%)
166238 1
< 0.1%
220102 1
< 0.1%
220385 1
< 0.1%
222175 1
< 0.1%
227359 1
< 0.1%
227362 1
< 0.1%
229477 1
< 0.1%
229508 1
< 0.1%
229753 1
< 0.1%
233197 1
< 0.1%
ValueCountFrequency (%)
3006812 1
< 0.1%
3006811 1
< 0.1%
3006810 1
< 0.1%
3006809 1
< 0.1%
3006808 1
< 0.1%
3006807 1
< 0.1%
3006806 1
< 0.1%
3006805 1
< 0.1%
3006804 1
< 0.1%
3006803 1
< 0.1%

DIRECTORIO_HOG
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct107119
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11166540
Minimum1662381
Maximum30068121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:45:53.230216image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1662381
5-th percentile3308490
Q19988096
median10534921
Q311510586
95-th percentile30006872
Maximum30068121
Range28405740
Interquartile range (IQR)1522490

Descriptive statistics

Standard deviation5980617.9
Coefficient of variation (CV)0.53558379
Kurtosis3.3293812
Mean11166540
Median Absolute Deviation (MAD)839040
Skewness1.576843
Sum1.1961486 × 1012
Variance3.5767791 × 1013
MonotonicityNot monotonic
2024-04-21T21:45:53.376010image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11653551 1
 
< 0.1%
10583531 1
 
< 0.1%
10583251 1
 
< 0.1%
10583241 1
 
< 0.1%
10583231 1
 
< 0.1%
10583221 1
 
< 0.1%
10583211 1
 
< 0.1%
10583201 1
 
< 0.1%
10583191 1
 
< 0.1%
10583181 1
 
< 0.1%
Other values (107109) 107109
> 99.9%
ValueCountFrequency (%)
1662381 1
< 0.1%
2201021 1
< 0.1%
2203851 1
< 0.1%
2221751 1
< 0.1%
2273591 1
< 0.1%
2273621 1
< 0.1%
2294771 1
< 0.1%
2295081 1
< 0.1%
2297531 1
< 0.1%
2331971 1
< 0.1%
ValueCountFrequency (%)
30068121 1
< 0.1%
30068111 1
< 0.1%
30068101 1
< 0.1%
30068091 1
< 0.1%
30068081 1
< 0.1%
30068071 1
< 0.1%
30068061 1
< 0.1%
30068051 1
< 0.1%
30068041 1
< 0.1%
30068031 1
< 0.1%

SECUENCIA_P
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0076364
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:45:53.494301image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.10896079
Coefficient of variation (CV)0.10813503
Kurtosis495.86853
Mean1.0076364
Median Absolute Deviation (MAD)0
Skewness19.288758
Sum107937
Variance0.011872453
MonotonicityNot monotonic
2024-04-21T21:45:53.600764image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 106467
99.4%
2 533
 
0.5%
3 86
 
0.1%
4 22
 
< 0.1%
5 8
 
< 0.1%
6 3
 
< 0.1%
ValueCountFrequency (%)
1 106467
99.4%
2 533
 
0.5%
3 86
 
0.1%
4 22
 
< 0.1%
5 8
 
< 0.1%
6 3
 
< 0.1%
ValueCountFrequency (%)
6 3
 
< 0.1%
5 8
 
< 0.1%
4 22
 
< 0.1%
3 86
 
0.1%
2 533
 
0.5%
1 106467
99.4%

NHCMP9A
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
84515 
1
22604 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 84515
78.9%
1 22604
 
21.1%

Length

2024-04-21T21:45:53.714209image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:45:53.809562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 84515
78.9%
1 22604
 
21.1%

Most occurring characters

ValueCountFrequency (%)
2 84515
78.9%
1 22604
 
21.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 84515
78.9%
1 22604
 
21.1%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 84515
78.9%
1 22604
 
21.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 84515
78.9%
1 22604
 
21.1%

NHCMP9AA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing84515
Missing (%)78.9%
Memory size837.0 KiB

NHCMP9AB
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct102
Distinct (%)0.5%
Missing84515
Missing (%)78.9%
Infinite0
Infinite (%)0.0%
Mean36953.145
Minimum0
Maximum8000000
Zeros20574
Zeros (%)19.2%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:45:53.928264image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile200000
Maximum8000000
Range8000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation222047.01
Coefficient of variation (CV)6.00888
Kurtosis249.95144
Mean36953.145
Median Absolute Deviation (MAD)0
Skewness12.850216
Sum8.3528889 × 108
Variance4.9304876 × 1010
MonotonicityNot monotonic
2024-04-21T21:45:54.083928image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20574
 
19.2%
99 307
 
0.3%
300000 199
 
0.2%
200000 195
 
0.2%
500000 156
 
0.1%
400000 115
 
0.1%
98 102
 
0.1%
100000 97
 
0.1%
250000 95
 
0.1%
150000 81
 
0.1%
Other values (92) 683
 
0.6%
(Missing) 84515
78.9%
ValueCountFrequency (%)
0 20574
19.2%
98 102
 
0.1%
99 307
 
0.3%
10000 6
 
< 0.1%
15000 1
 
< 0.1%
20000 4
 
< 0.1%
25000 3
 
< 0.1%
30000 9
 
< 0.1%
35000 3
 
< 0.1%
38000 1
 
< 0.1%
ValueCountFrequency (%)
8000000 1
 
< 0.1%
6000000 2
 
< 0.1%
5800000 1
 
< 0.1%
5000000 3
 
< 0.1%
4000000 6
 
< 0.1%
3050000 1
 
< 0.1%
3000000 20
< 0.1%
2500000 9
< 0.1%
2300000 1
 
< 0.1%
2200000 1
 
< 0.1%

NHCMP9B
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
104633 
1
 
2486

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 104633
97.7%
1 2486
 
2.3%

Length

2024-04-21T21:45:54.224753image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:45:54.322983image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 104633
97.7%
1 2486
 
2.3%

Most occurring characters

ValueCountFrequency (%)
2 104633
97.7%
1 2486
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 104633
97.7%
1 2486
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 104633
97.7%
1 2486
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 104633
97.7%
1 2486
 
2.3%

NHCMP9BA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct107
Distinct (%)4.3%
Missing104633
Missing (%)97.7%
Infinite0
Infinite (%)0.0%
Mean77340.228
Minimum0
Maximum3500000
Zeros31
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:45:54.439188image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5000
Q115000
median30000
Q370000
95-th percentile300000
Maximum3500000
Range3500000
Interquartile range (IQR)55000

Descriptive statistics

Standard deviation169160.76
Coefficient of variation (CV)2.1872286
Kurtosis116.25203
Mean77340.228
Median Absolute Deviation (MAD)20000
Skewness8.6186381
Sum1.9226781 × 108
Variance2.8615363 × 1010
MonotonicityNot monotonic
2024-04-21T21:45:54.593114image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000 318
 
0.3%
50000 245
 
0.2%
30000 239
 
0.2%
15000 173
 
0.2%
10000 169
 
0.2%
100000 149
 
0.1%
25000 82
 
0.1%
40000 81
 
0.1%
200000 81
 
0.1%
60000 75
 
0.1%
Other values (97) 874
 
0.8%
(Missing) 104633
97.7%
ValueCountFrequency (%)
0 31
< 0.1%
98 20
 
< 0.1%
99 60
0.1%
5000 63
0.1%
6000 27
< 0.1%
6500 1
 
< 0.1%
7000 17
 
< 0.1%
7500 2
 
< 0.1%
8000 34
< 0.1%
8500 1
 
< 0.1%
ValueCountFrequency (%)
3500000 1
 
< 0.1%
2500000 1
 
< 0.1%
2000000 3
 
< 0.1%
1600000 1
 
< 0.1%
1500000 3
 
< 0.1%
1200000 1
 
< 0.1%
1000000 9
< 0.1%
800000 1
 
< 0.1%
750000 2
 
< 0.1%
748000 1
 
< 0.1%

NHCMP9BB
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct28
Distinct (%)1.1%
Missing104633
Missing (%)97.7%
Infinite0
Infinite (%)0.0%
Mean4645.4312
Minimum0
Maximum2000000
Zeros2376
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:45:54.732251image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2000000
Range2000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation54392.901
Coefficient of variation (CV)11.708903
Kurtosis789.2301
Mean4645.4312
Median Absolute Deviation (MAD)0
Skewness24.481093
Sum11548542
Variance2.9585877 × 109
MonotonicityNot monotonic
2024-04-21T21:45:54.868763image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 2376
 
2.2%
99 34
 
< 0.1%
98 12
 
< 0.1%
50000 8
 
< 0.1%
20000 7
 
< 0.1%
300000 6
 
< 0.1%
30000 6
 
< 0.1%
100000 5
 
< 0.1%
150000 5
 
< 0.1%
25000 4
 
< 0.1%
Other values (18) 23
 
< 0.1%
(Missing) 104633
97.7%
ValueCountFrequency (%)
0 2376
2.2%
98 12
 
< 0.1%
99 34
 
< 0.1%
10000 2
 
< 0.1%
12000 1
 
< 0.1%
15000 1
 
< 0.1%
20000 7
 
< 0.1%
25000 4
 
< 0.1%
30000 6
 
< 0.1%
35000 1
 
< 0.1%
ValueCountFrequency (%)
2000000 1
 
< 0.1%
1000000 1
 
< 0.1%
600000 1
 
< 0.1%
500000 1
 
< 0.1%
450000 1
 
< 0.1%
400000 1
 
< 0.1%
390000 1
 
< 0.1%
350000 1
 
< 0.1%
300000 6
< 0.1%
280000 2
 
< 0.1%

NHCMP9C
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
101136 
1
 
5983

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 101136
94.4%
1 5983
 
5.6%

Length

2024-04-21T21:45:55.001101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:45:55.095260image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 101136
94.4%
1 5983
 
5.6%

Most occurring characters

ValueCountFrequency (%)
2 101136
94.4%
1 5983
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 101136
94.4%
1 5983
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 101136
94.4%
1 5983
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 101136
94.4%
1 5983
 
5.6%

NHCMP9CA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing101136
Missing (%)94.4%
Memory size837.0 KiB

NHCMP9CB
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct60
Distinct (%)1.0%
Missing101136
Missing (%)94.4%
Infinite0
Infinite (%)0.0%
Mean6942.6102
Minimum0
Maximum3000000
Zeros5612
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:45:55.209025image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile99
Maximum3000000
Range3000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation63077.561
Coefficient of variation (CV)9.0855686
Kurtosis965.8391
Mean6942.6102
Median Absolute Deviation (MAD)0
Skewness25.097884
Sum41537637
Variance3.9787788 × 109
MonotonicityNot monotonic
2024-04-21T21:45:55.359331image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5612
 
5.2%
99 117
 
0.1%
100000 26
 
< 0.1%
200000 25
 
< 0.1%
98 23
 
< 0.1%
50000 14
 
< 0.1%
30000 12
 
< 0.1%
150000 12
 
< 0.1%
80000 10
 
< 0.1%
300000 10
 
< 0.1%
Other values (50) 122
 
0.1%
(Missing) 101136
94.4%
ValueCountFrequency (%)
0 5612
5.2%
98 23
 
< 0.1%
99 117
 
0.1%
10000 4
 
< 0.1%
12000 2
 
< 0.1%
13000 1
 
< 0.1%
15000 1
 
< 0.1%
18000 1
 
< 0.1%
20000 6
 
< 0.1%
21000 1
 
< 0.1%
ValueCountFrequency (%)
3000000 1
 
< 0.1%
1500000 1
 
< 0.1%
1200000 2
 
< 0.1%
1000000 1
 
< 0.1%
700000 1
 
< 0.1%
600000 3
< 0.1%
500000 6
< 0.1%
450000 1
 
< 0.1%
400000 6
< 0.1%
380000 2
 
< 0.1%

NHCMP9D
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
106232 
1
 
887

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 106232
99.2%
1 887
 
0.8%

Length

2024-04-21T21:45:55.497837image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:45:55.592824image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 106232
99.2%
1 887
 
0.8%

Most occurring characters

ValueCountFrequency (%)
2 106232
99.2%
1 887
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 106232
99.2%
1 887
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 106232
99.2%
1 887
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 106232
99.2%
1 887
 
0.8%

NHCMP9DA
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct92
Distinct (%)10.4%
Missing106232
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean226791.51
Minimum0
Maximum30000000
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:45:55.706261image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5000
Q130000
median60000
Q3150000
95-th percentile685000
Maximum30000000
Range30000000
Interquartile range (IQR)120000

Descriptive statistics

Standard deviation1112951.9
Coefficient of variation (CV)4.9073791
Kurtosis580.81553
Mean226791.51
Median Absolute Deviation (MAD)40000
Skewness22.17628
Sum2.0116407 × 108
Variance1.2386619 × 1012
MonotonicityNot monotonic
2024-04-21T21:45:55.854989image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 93
 
0.1%
100000 82
 
0.1%
30000 70
 
0.1%
20000 54
 
0.1%
200000 49
 
< 0.1%
150000 49
 
< 0.1%
60000 47
 
< 0.1%
300000 42
 
< 0.1%
80000 29
 
< 0.1%
10000 27
 
< 0.1%
Other values (82) 345
 
0.3%
(Missing) 106232
99.2%
ValueCountFrequency (%)
0 8
 
< 0.1%
98 6
 
< 0.1%
99 22
< 0.1%
5000 17
< 0.1%
6000 3
 
< 0.1%
7000 2
 
< 0.1%
8000 4
 
< 0.1%
8500 1
 
< 0.1%
9000 1
 
< 0.1%
10000 27
< 0.1%
ValueCountFrequency (%)
30000000 1
 
< 0.1%
5000000 3
< 0.1%
4000000 3
< 0.1%
3000000 3
< 0.1%
2800000 1
 
< 0.1%
2500000 1
 
< 0.1%
2165000 1
 
< 0.1%
2000000 4
< 0.1%
1800000 1
 
< 0.1%
1600000 1
 
< 0.1%

NHCMP9DB
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)1.7%
Missing106232
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean6033.0147
Minimum0
Maximum2000000
Zeros861
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:45:55.985607image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2000000
Range2000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation81850.879
Coefficient of variation (CV)13.567161
Kurtosis427.53994
Mean6033.0147
Median Absolute Deviation (MAD)0
Skewness19.299741
Sum5351284
Variance6.6995663 × 109
MonotonicityNot monotonic
2024-04-21T21:45:56.111840image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 861
 
0.8%
99 10
 
< 0.1%
98 3
 
< 0.1%
80000 2
 
< 0.1%
350000 1
 
< 0.1%
450000 1
 
< 0.1%
30000 1
 
< 0.1%
50000 1
 
< 0.1%
10000 1
 
< 0.1%
20000 1
 
< 0.1%
Other values (5) 5
 
< 0.1%
(Missing) 106232
99.2%
ValueCountFrequency (%)
0 861
0.8%
98 3
 
< 0.1%
99 10
 
< 0.1%
10000 1
 
< 0.1%
20000 1
 
< 0.1%
30000 1
 
< 0.1%
50000 1
 
< 0.1%
80000 2
 
< 0.1%
200000 1
 
< 0.1%
350000 1
 
< 0.1%
ValueCountFrequency (%)
2000000 1
< 0.1%
1000000 1
< 0.1%
580000 1
< 0.1%
500000 1
< 0.1%
450000 1
< 0.1%
350000 1
< 0.1%
200000 1
< 0.1%
80000 2
< 0.1%
50000 1
< 0.1%
30000 1
< 0.1%

NHCMP9E
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
85584 
1
21535 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 85584
79.9%
1 21535
 
20.1%

Length

2024-04-21T21:45:56.239052image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:45:56.334895image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 85584
79.9%
1 21535
 
20.1%

Most occurring characters

ValueCountFrequency (%)
2 85584
79.9%
1 21535
 
20.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 85584
79.9%
1 21535
 
20.1%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 85584
79.9%
1 21535
 
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 85584
79.9%
1 21535
 
20.1%

NHCMP9EA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing85584
Missing (%)79.9%
Memory size837.0 KiB

NHCMP10
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
106691 
1
 
428

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 106691
99.6%
1 428
 
0.4%

Length

2024-04-21T21:45:56.439627image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:45:56.533552image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 106691
99.6%
1 428
 
0.4%

Most occurring characters

ValueCountFrequency (%)
2 106691
99.6%
1 428
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 106691
99.6%
1 428
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 106691
99.6%
1 428
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 106691
99.6%
1 428
 
0.4%

NHCMP10A_1
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing106735
Missing (%)99.6%
Memory size837.0 KiB
1.0
384 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1152
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 384
 
0.4%
(Missing) 106735
99.6%

Length

2024-04-21T21:45:56.630579image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:45:56.725672image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 384
100.0%

Most occurring characters

ValueCountFrequency (%)
1 384
33.3%
. 384
33.3%
0 384
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 768
66.7%
Other Punctuation 384
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 384
50.0%
0 384
50.0%
Other Punctuation
ValueCountFrequency (%)
. 384
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1152
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 384
33.3%
. 384
33.3%
0 384
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 384
33.3%
. 384
33.3%
0 384
33.3%

NHCMP10A_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)5.9%
Missing107102
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
17 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters51
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 17
 
< 0.1%
(Missing) 107102
> 99.9%

Length

2024-04-21T21:45:56.831701image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:45:56.935603image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 17
100.0%

Most occurring characters

ValueCountFrequency (%)
1 17
33.3%
. 17
33.3%
0 17
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34
66.7%
Other Punctuation 17
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 17
50.0%
0 17
50.0%
Other Punctuation
ValueCountFrequency (%)
. 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 51
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 17
33.3%
. 17
33.3%
0 17
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 17
33.3%
. 17
33.3%
0 17
33.3%

NHCMP10A_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.0%
Missing107086
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
33 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters99
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 33
 
< 0.1%
(Missing) 107086
> 99.9%

Length

2024-04-21T21:45:57.041575image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:45:57.137193image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 33
100.0%

Most occurring characters

ValueCountFrequency (%)
1 33
33.3%
. 33
33.3%
0 33
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66
66.7%
Other Punctuation 33
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 33
50.0%
0 33
50.0%
Other Punctuation
ValueCountFrequency (%)
. 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 99
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 33
33.3%
. 33
33.3%
0 33
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 33
33.3%
. 33
33.3%
0 33
33.3%

NHCMP10A_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)10.0%
Missing107109
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
10 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 10
 
< 0.1%
(Missing) 107109
> 99.9%

Length

2024-04-21T21:45:57.235313image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:45:57.324748image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 10
100.0%

Most occurring characters

ValueCountFrequency (%)
1 10
33.3%
. 10
33.3%
0 10
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20
66.7%
Other Punctuation 10
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10
50.0%
0 10
50.0%
Other Punctuation
ValueCountFrequency (%)
. 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10
33.3%
. 10
33.3%
0 10
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10
33.3%
. 10
33.3%
0 10
33.3%

NHCMP10A_5
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)7.7%
Missing107106
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
13 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters39
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 13
 
< 0.1%
(Missing) 107106
> 99.9%

Length

2024-04-21T21:45:57.420925image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:45:57.516426image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 13
100.0%

Most occurring characters

ValueCountFrequency (%)
1 13
33.3%
. 13
33.3%
0 13
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26
66.7%
Other Punctuation 13
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13
50.0%
0 13
50.0%
Other Punctuation
ValueCountFrequency (%)
. 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13
33.3%
. 13
33.3%
0 13
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13
33.3%
. 13
33.3%
0 13
33.3%

NHCMP10AAA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct44
Distinct (%)11.5%
Missing106735
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean141370.55
Minimum98
Maximum1400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:45:57.624092image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile99
Q125000
median100000
Q3200000
95-th percentile500000
Maximum1400000
Range1399902
Interquartile range (IQR)175000

Descriptive statistics

Standard deviation172772.46
Coefficient of variation (CV)1.2221249
Kurtosis11.852773
Mean141370.55
Median Absolute Deviation (MAD)99901
Skewness2.7571911
Sum54286291
Variance2.9850324 × 1010
MonotonicityNot monotonic
2024-04-21T21:45:57.768681image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
99 77
 
0.1%
200000 49
 
< 0.1%
100000 46
 
< 0.1%
50000 28
 
< 0.1%
300000 21
 
< 0.1%
60000 17
 
< 0.1%
150000 16
 
< 0.1%
80000 14
 
< 0.1%
500000 12
 
< 0.1%
250000 10
 
< 0.1%
Other values (34) 94
 
0.1%
(Missing) 106735
99.6%
ValueCountFrequency (%)
98 6
 
< 0.1%
99 77
0.1%
10000 3
 
< 0.1%
12000 1
 
< 0.1%
20000 7
 
< 0.1%
25000 3
 
< 0.1%
30000 9
 
< 0.1%
35000 2
 
< 0.1%
40000 3
 
< 0.1%
45000 1
 
< 0.1%
ValueCountFrequency (%)
1400000 1
 
< 0.1%
1000000 3
 
< 0.1%
800000 1
 
< 0.1%
790000 1
 
< 0.1%
600000 4
 
< 0.1%
500000 12
< 0.1%
450000 1
 
< 0.1%
400000 7
< 0.1%
380000 2
 
< 0.1%
350000 7
< 0.1%

NHCMP10AAB_1
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)20.0%
Missing107114
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5
 
< 0.1%
(Missing) 107114
> 99.9%

Length

2024-04-21T21:45:57.901774image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:45:57.991637image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5
100.0%

Most occurring characters

ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
66.7%
Other Punctuation 5
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5
50.0%
0 5
50.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

NHCMP10AAB_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)9.1%
Missing107108
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
11 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters33
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 11
 
< 0.1%
(Missing) 107108
> 99.9%

Length

2024-04-21T21:45:58.088400image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:45:58.179657image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 11
100.0%

Most occurring characters

ValueCountFrequency (%)
1 11
33.3%
. 11
33.3%
0 11
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22
66.7%
Other Punctuation 11
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11
50.0%
0 11
50.0%
Other Punctuation
ValueCountFrequency (%)
. 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 11
33.3%
. 11
33.3%
0 11
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11
33.3%
. 11
33.3%
0 11
33.3%

NHCMP10AAB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing106745
Missing (%)99.7%
Memory size837.0 KiB
1.0
374 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1122
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 374
 
0.3%
(Missing) 106745
99.7%

Length

2024-04-21T21:45:58.278857image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:45:58.379322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 374
100.0%

Most occurring characters

ValueCountFrequency (%)
1 374
33.3%
. 374
33.3%
0 374
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 748
66.7%
Other Punctuation 374
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 374
50.0%
0 374
50.0%
Other Punctuation
ValueCountFrequency (%)
. 374
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1122
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 374
33.3%
. 374
33.3%
0 374
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1122
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 374
33.3%
. 374
33.3%
0 374
33.3%

NHCMP10AAB_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing107117
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0

Common Values

ValueCountFrequency (%)
1.0 2
 
< 0.1%
(Missing) 107117
> 99.9%

Length

2024-04-21T21:45:58.480753image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:45:58.577653image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
66.7%
Other Punctuation 2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2
50.0%
0 2
50.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

NHCMP10ABA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)47.1%
Missing107102
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean100317.41
Minimum99
Maximum500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:45:58.669054image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum99
5-th percentile99
Q125000
median50000
Q3150000
95-th percentile260000
Maximum500000
Range499901
Interquartile range (IQR)125000

Descriptive statistics

Standard deviation125235.81
Coefficient of variation (CV)1.2483955
Kurtosis6.0757717
Mean100317.41
Median Absolute Deviation (MAD)49901
Skewness2.2301953
Sum1705396
Variance1.5684008 × 1010
MonotonicityNot monotonic
2024-04-21T21:45:58.776011image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
50000 5
 
< 0.1%
99 4
 
< 0.1%
200000 3
 
< 0.1%
80000 1
 
< 0.1%
500000 1
 
< 0.1%
100000 1
 
< 0.1%
25000 1
 
< 0.1%
150000 1
 
< 0.1%
(Missing) 107102
> 99.9%
ValueCountFrequency (%)
99 4
< 0.1%
25000 1
 
< 0.1%
50000 5
< 0.1%
80000 1
 
< 0.1%
100000 1
 
< 0.1%
150000 1
 
< 0.1%
200000 3
< 0.1%
500000 1
 
< 0.1%
ValueCountFrequency (%)
500000 1
 
< 0.1%
200000 3
< 0.1%
150000 1
 
< 0.1%
100000 1
 
< 0.1%
80000 1
 
< 0.1%
50000 5
< 0.1%
25000 1
 
< 0.1%
99 4
< 0.1%

NHCMP10ABB_1
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:45:58.893023image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:45:58.986374image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP10ABB_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:45:59.085943image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:45:59.178031image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP10ABB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)6.7%
Missing107104
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
15 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 15
 
< 0.1%
(Missing) 107104
> 99.9%

Length

2024-04-21T21:45:59.276797image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:45:59.374021image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 15
100.0%

Most occurring characters

ValueCountFrequency (%)
1 15
33.3%
. 15
33.3%
0 15
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30
66.7%
Other Punctuation 15
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 15
50.0%
0 15
50.0%
Other Punctuation
ValueCountFrequency (%)
. 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 15
33.3%
. 15
33.3%
0 15
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 15
33.3%
. 15
33.3%
0 15
33.3%

NHCMP10ABB_4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP10ACA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)33.3%
Missing107086
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean33457.182
Minimum98
Maximum200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:45:59.463958image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile98.6
Q199
median20000
Q350000
95-th percentile100000
Maximum200000
Range199902
Interquartile range (IQR)49901

Descriptive statistics

Standard deviation44296.398
Coefficient of variation (CV)1.3239728
Kurtosis5.0561983
Mean33457.182
Median Absolute Deviation (MAD)19901
Skewness2.020068
Sum1104087
Variance1.9621709 × 109
MonotonicityNot monotonic
2024-04-21T21:45:59.586395image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
99 9
 
< 0.1%
20000 5
 
< 0.1%
10000 4
 
< 0.1%
100000 3
 
< 0.1%
60000 2
 
< 0.1%
98 2
 
< 0.1%
80000 2
 
< 0.1%
50000 2
 
< 0.1%
30000 2
 
< 0.1%
23000 1
 
< 0.1%
(Missing) 107086
> 99.9%
ValueCountFrequency (%)
98 2
 
< 0.1%
99 9
< 0.1%
10000 4
< 0.1%
20000 5
< 0.1%
23000 1
 
< 0.1%
30000 2
 
< 0.1%
50000 2
 
< 0.1%
60000 2
 
< 0.1%
80000 2
 
< 0.1%
100000 3
 
< 0.1%
ValueCountFrequency (%)
200000 1
 
< 0.1%
100000 3
 
< 0.1%
80000 2
 
< 0.1%
60000 2
 
< 0.1%
50000 2
 
< 0.1%
30000 2
 
< 0.1%
23000 1
 
< 0.1%
20000 5
< 0.1%
10000 4
< 0.1%
99 9
< 0.1%

NHCMP10ACB_1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP10ACB_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:45:59.717243image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:45:59.809084image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP10ACB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.1%
Missing107087
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
32 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters96
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 32
 
< 0.1%
(Missing) 107087
> 99.9%

Length

2024-04-21T21:45:59.906986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:00.000480image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 32
100.0%

Most occurring characters

ValueCountFrequency (%)
1 32
33.3%
. 32
33.3%
0 32
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 64
66.7%
Other Punctuation 32
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 32
50.0%
0 32
50.0%
Other Punctuation
ValueCountFrequency (%)
. 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 96
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 32
33.3%
. 32
33.3%
0 32
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 96
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 32
33.3%
. 32
33.3%
0 32
33.3%

NHCMP10ACB_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:00.099782image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:00.190200image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP10ADA
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)50.0%
Missing107109
Missing (%)> 99.9%
Memory size837.0 KiB
99.0
100000.0
90000.0
20000.0
98.0

Length

Max length8
Median length4
Mean length5.4
Min length4

Characters and Unicode

Total characters54
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)30.0%

Sample

1st row99.0
2nd row99.0
3rd row90000.0
4th row99.0
5th row99.0

Common Values

ValueCountFrequency (%)
99.0 5
 
< 0.1%
100000.0 2
 
< 0.1%
90000.0 1
 
< 0.1%
20000.0 1
 
< 0.1%
98.0 1
 
< 0.1%
(Missing) 107109
> 99.9%

Length

2024-04-21T21:46:00.302032image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:00.420798image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
99.0 5
50.0%
100000.0 2
 
20.0%
90000.0 1
 
10.0%
20000.0 1
 
10.0%
98.0 1
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 28
51.9%
9 12
22.2%
. 10
 
18.5%
1 2
 
3.7%
2 1
 
1.9%
8 1
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44
81.5%
Other Punctuation 10
 
18.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28
63.6%
9 12
27.3%
1 2
 
4.5%
2 1
 
2.3%
8 1
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 54
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28
51.9%
9 12
22.2%
. 10
 
18.5%
1 2
 
3.7%
2 1
 
1.9%
8 1
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28
51.9%
9 12
22.2%
. 10
 
18.5%
1 2
 
3.7%
2 1
 
1.9%
8 1
 
1.9%

NHCMP10ADB_1
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:00.540426image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:00.631386image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP10ADB_2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP10ADB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)12.5%
Missing107111
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 8
 
< 0.1%
(Missing) 107111
> 99.9%

Length

2024-04-21T21:46:00.726336image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:00.820720image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 8
100.0%

Most occurring characters

ValueCountFrequency (%)
1 8
33.3%
. 8
33.3%
0 8
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16
66.7%
Other Punctuation 8
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8
50.0%
0 8
50.0%
Other Punctuation
ValueCountFrequency (%)
. 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8
33.3%
. 8
33.3%
0 8
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8
33.3%
. 8
33.3%
0 8
33.3%

NHCMP10ADB_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:00.922961image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:01.012099image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP10AEA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)69.2%
Missing107106
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean173453.62
Minimum99
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:46:01.101036image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum99
5-th percentile99
Q13000
median33000
Q380000
95-th percentile700000
Maximum1000000
Range999901
Interquartile range (IQR)77000

Descriptive statistics

Standard deviation306229.1
Coefficient of variation (CV)1.7654812
Kurtosis3.8406005
Mean173453.62
Median Absolute Deviation (MAD)32901
Skewness2.0500623
Sum2254897
Variance9.3776263 × 1010
MonotonicityNot monotonic
2024-04-21T21:46:01.213434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
99 3
 
< 0.1%
80000 2
 
< 0.1%
500000 2
 
< 0.1%
4000 1
 
< 0.1%
4600 1
 
< 0.1%
50000 1
 
< 0.1%
33000 1
 
< 0.1%
1000000 1
 
< 0.1%
3000 1
 
< 0.1%
(Missing) 107106
> 99.9%
ValueCountFrequency (%)
99 3
< 0.1%
3000 1
 
< 0.1%
4000 1
 
< 0.1%
4600 1
 
< 0.1%
33000 1
 
< 0.1%
50000 1
 
< 0.1%
80000 2
< 0.1%
500000 2
< 0.1%
1000000 1
 
< 0.1%
ValueCountFrequency (%)
1000000 1
 
< 0.1%
500000 2
< 0.1%
80000 2
< 0.1%
50000 1
 
< 0.1%
33000 1
 
< 0.1%
4600 1
 
< 0.1%
4000 1
 
< 0.1%
3000 1
 
< 0.1%
99 3
< 0.1%

NHCMP10AEB_1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP10AEB_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)20.0%
Missing107114
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5
 
< 0.1%
(Missing) 107114
> 99.9%

Length

2024-04-21T21:46:01.333419image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:01.424611image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5
100.0%

Most occurring characters

ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
66.7%
Other Punctuation 5
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5
50.0%
0 5
50.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

NHCMP10AEB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)14.3%
Missing107112
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 7
 
< 0.1%
(Missing) 107112
> 99.9%

Length

2024-04-21T21:46:01.523067image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:01.618964image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7
100.0%

Most occurring characters

ValueCountFrequency (%)
1 7
33.3%
. 7
33.3%
0 7
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14
66.7%
Other Punctuation 7
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7
50.0%
0 7
50.0%
Other Punctuation
ValueCountFrequency (%)
. 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7
33.3%
. 7
33.3%
0 7
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7
33.3%
. 7
33.3%
0 7
33.3%

NHCMP10AEB_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:01.718609image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:01.810887image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP11A
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
99671 
1
 
7448

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 99671
93.0%
1 7448
 
7.0%

Length

2024-04-21T21:46:01.908395image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:02.000502image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 99671
93.0%
1 7448
 
7.0%

Most occurring characters

ValueCountFrequency (%)
2 99671
93.0%
1 7448
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 99671
93.0%
1 7448
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 99671
93.0%
1 7448
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 99671
93.0%
1 7448
 
7.0%

NHCMP11AA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing99671
Missing (%)93.0%
Memory size837.0 KiB

NHCMP11AB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing99671
Missing (%)93.0%
Memory size837.0 KiB

NHCMP11B
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
99382 
1
 
7737

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 99382
92.8%
1 7737
 
7.2%

Length

2024-04-21T21:46:02.101017image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:02.196178image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 99382
92.8%
1 7737
 
7.2%

Most occurring characters

ValueCountFrequency (%)
2 99382
92.8%
1 7737
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 99382
92.8%
1 7737
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 99382
92.8%
1 7737
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 99382
92.8%
1 7737
 
7.2%

NHCMP11BA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing99382
Missing (%)92.8%
Memory size837.0 KiB

NHCMP11BB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing99382
Missing (%)92.8%
Memory size837.0 KiB

NHCMP11C
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
102190 
1
 
4929

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 102190
95.4%
1 4929
 
4.6%

Length

2024-04-21T21:46:02.300441image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:02.396196image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 102190
95.4%
1 4929
 
4.6%

Most occurring characters

ValueCountFrequency (%)
2 102190
95.4%
1 4929
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 102190
95.4%
1 4929
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 102190
95.4%
1 4929
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 102190
95.4%
1 4929
 
4.6%

NHCMP11CA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing102190
Missing (%)95.4%
Memory size837.0 KiB

NHCMP11CB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing102190
Missing (%)95.4%
Memory size837.0 KiB

NHCMP11D
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
102016 
1
 
5103

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 102016
95.2%
1 5103
 
4.8%

Length

2024-04-21T21:46:02.496499image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:02.591211image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 102016
95.2%
1 5103
 
4.8%

Most occurring characters

ValueCountFrequency (%)
2 102016
95.2%
1 5103
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 102016
95.2%
1 5103
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 102016
95.2%
1 5103
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 102016
95.2%
1 5103
 
4.8%

NHCMP11DA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing102016
Missing (%)95.2%
Memory size837.0 KiB

NHCMP11DB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing102016
Missing (%)95.2%
Memory size837.0 KiB

NHCMP11E
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
104989 
1
 
2130

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 104989
98.0%
1 2130
 
2.0%

Length

2024-04-21T21:46:02.698519image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:02.794748image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 104989
98.0%
1 2130
 
2.0%

Most occurring characters

ValueCountFrequency (%)
2 104989
98.0%
1 2130
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 104989
98.0%
1 2130
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 104989
98.0%
1 2130
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 104989
98.0%
1 2130
 
2.0%

NHCMP11EA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing104989
Missing (%)98.0%
Memory size837.0 KiB

NHCMP11EB
Text

MISSING 

Distinct186
Distinct (%)8.7%
Missing104989
Missing (%)98.0%
Memory size837.0 KiB
2024-04-21T21:46:02.944664image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length8
Median length1
Mean length2.6971831
Min length1

Characters and Unicode

Total characters5745
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)3.3%

Sample

1st row0
2nd row0
3rd row7e+07
4th row8e+07
5th row0
ValueCountFrequency (%)
0 1369
64.3%
99 54
 
2.5%
2e+07 20
 
0.9%
6e+06 19
 
0.9%
7e+06 19
 
0.9%
3e+07 19
 
0.9%
5e+06 18
 
0.8%
5e+07 17
 
0.8%
4e+07 16
 
0.8%
1,5e+07 16
 
0.8%
Other values (176) 563
26.4%
2024-04-21T21:46:03.281789image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2605
45.3%
e 548
 
9.5%
+ 548
 
9.5%
7 431
 
7.5%
5 251
 
4.4%
, 251
 
4.4%
6 218
 
3.8%
1 184
 
3.2%
9 166
 
2.9%
2 158
 
2.8%
Other values (3) 385
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4398
76.6%
Lowercase Letter 548
 
9.5%
Math Symbol 548
 
9.5%
Other Punctuation 251
 
4.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2605
59.2%
7 431
 
9.8%
5 251
 
5.7%
6 218
 
5.0%
1 184
 
4.2%
9 166
 
3.8%
2 158
 
3.6%
8 149
 
3.4%
3 125
 
2.8%
4 111
 
2.5%
Lowercase Letter
ValueCountFrequency (%)
e 548
100.0%
Math Symbol
ValueCountFrequency (%)
+ 548
100.0%
Other Punctuation
ValueCountFrequency (%)
, 251
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5197
90.5%
Latin 548
 
9.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2605
50.1%
+ 548
 
10.5%
7 431
 
8.3%
5 251
 
4.8%
, 251
 
4.8%
6 218
 
4.2%
1 184
 
3.5%
9 166
 
3.2%
2 158
 
3.0%
8 149
 
2.9%
Other values (2) 236
 
4.5%
Latin
ValueCountFrequency (%)
e 548
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5745
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2605
45.3%
e 548
 
9.5%
+ 548
 
9.5%
7 431
 
7.5%
5 251
 
4.4%
, 251
 
4.4%
6 218
 
3.8%
1 184
 
3.2%
9 166
 
2.9%
2 158
 
2.8%
Other values (3) 385
 
6.7%

NHCMP11F
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
106012 
1
 
1107

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 106012
99.0%
1 1107
 
1.0%

Length

2024-04-21T21:46:03.437952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:03.532249image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 106012
99.0%
1 1107
 
1.0%

Most occurring characters

ValueCountFrequency (%)
2 106012
99.0%
1 1107
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 106012
99.0%
1 1107
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 106012
99.0%
1 1107
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 106012
99.0%
1 1107
 
1.0%

NHCMP11FA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing106012
Missing (%)99.0%
Memory size837.0 KiB

NHCMP11FB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing106012
Missing (%)99.0%
Memory size837.0 KiB

NHCMP11G
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
97155 
1
9964 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 97155
90.7%
1 9964
 
9.3%

Length

2024-04-21T21:46:03.632736image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:03.728119image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 97155
90.7%
1 9964
 
9.3%

Most occurring characters

ValueCountFrequency (%)
2 97155
90.7%
1 9964
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 97155
90.7%
1 9964
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 97155
90.7%
1 9964
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 97155
90.7%
1 9964
 
9.3%

NHCMP11GA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing97155
Missing (%)90.7%
Memory size837.0 KiB

NHCMP11GB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing97155
Missing (%)90.7%
Memory size837.0 KiB

NHCMP11H
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
102741 
1
 
4378

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 102741
95.9%
1 4378
 
4.1%

Length

2024-04-21T21:46:03.835134image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:03.931206image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 102741
95.9%
1 4378
 
4.1%

Most occurring characters

ValueCountFrequency (%)
2 102741
95.9%
1 4378
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 102741
95.9%
1 4378
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 102741
95.9%
1 4378
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 102741
95.9%
1 4378
 
4.1%

NHCMP11HA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing102741
Missing (%)95.9%
Memory size837.0 KiB

NHCMP11HB
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct50
Distinct (%)1.1%
Missing102741
Missing (%)95.9%
Infinite0
Infinite (%)0.0%
Mean36481.591
Minimum0
Maximum5000000
Zeros4085
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:46:04.048269image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile99
Maximum5000000
Range5000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation266585.12
Coefficient of variation (CV)7.3073873
Kurtosis176.12947
Mean36481.591
Median Absolute Deviation (MAD)0
Skewness12.006558
Sum1.5971641 × 108
Variance7.1067625 × 1010
MonotonicityNot monotonic
2024-04-21T21:46:04.198516image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4085
 
3.8%
99 57
 
0.1%
500000 30
 
< 0.1%
200000 24
 
< 0.1%
98 18
 
< 0.1%
300000 17
 
< 0.1%
1000000 17
 
< 0.1%
100000 14
 
< 0.1%
400000 10
 
< 0.1%
150000 9
 
< 0.1%
Other values (40) 97
 
0.1%
(Missing) 102741
95.9%
ValueCountFrequency (%)
0 4085
3.8%
98 18
 
< 0.1%
99 57
 
0.1%
2000 1
 
< 0.1%
15000 1
 
< 0.1%
20000 1
 
< 0.1%
30000 4
 
< 0.1%
40000 1
 
< 0.1%
45000 1
 
< 0.1%
50000 2
 
< 0.1%
ValueCountFrequency (%)
5000000 4
< 0.1%
4000000 3
< 0.1%
3800000 1
 
< 0.1%
3000000 4
< 0.1%
2500000 1
 
< 0.1%
2200000 2
 
< 0.1%
2100000 1
 
< 0.1%
2000000 5
< 0.1%
1800000 2
 
< 0.1%
1700000 2
 
< 0.1%

NHCMP11I
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
101184 
1
 
5935

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 101184
94.5%
1 5935
 
5.5%

Length

2024-04-21T21:46:04.329032image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:04.427346image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 101184
94.5%
1 5935
 
5.5%

Most occurring characters

ValueCountFrequency (%)
2 101184
94.5%
1 5935
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 101184
94.5%
1 5935
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 101184
94.5%
1 5935
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 101184
94.5%
1 5935
 
5.5%

NHCMP11IA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing101184
Missing (%)94.5%
Memory size837.0 KiB

NHCMP11IB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing101184
Missing (%)94.5%
Memory size837.0 KiB

NHCMP11J
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
101802 
1
 
5317

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 101802
95.0%
1 5317
 
5.0%

Length

2024-04-21T21:46:04.529769image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:04.623184image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 101802
95.0%
1 5317
 
5.0%

Most occurring characters

ValueCountFrequency (%)
2 101802
95.0%
1 5317
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 101802
95.0%
1 5317
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 101802
95.0%
1 5317
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 101802
95.0%
1 5317
 
5.0%

NHCMP11JA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing101802
Missing (%)95.0%
Memory size837.0 KiB

NHCMP11JB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing101802
Missing (%)95.0%
Memory size837.0 KiB

NHCMP11K
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
99480 
1
 
7639

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 99480
92.9%
1 7639
 
7.1%

Length

2024-04-21T21:46:04.728324image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:04.823201image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 99480
92.9%
1 7639
 
7.1%

Most occurring characters

ValueCountFrequency (%)
2 99480
92.9%
1 7639
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 99480
92.9%
1 7639
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 99480
92.9%
1 7639
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 99480
92.9%
1 7639
 
7.1%

NHCMP11KA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing99480
Missing (%)92.9%
Memory size837.0 KiB

NHCMP11KB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing99480
Missing (%)92.9%
Memory size837.0 KiB

NHCMPA11AL
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
68057 
1
39062 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 68057
63.5%
1 39062
36.5%

Length

2024-04-21T21:46:04.923400image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:05.019067image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 68057
63.5%
1 39062
36.5%

Most occurring characters

ValueCountFrequency (%)
2 68057
63.5%
1 39062
36.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 68057
63.5%
1 39062
36.5%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 68057
63.5%
1 39062
36.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 68057
63.5%
1 39062
36.5%

NHCMPA11ALA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing68057
Missing (%)63.5%
Memory size837.0 KiB

NHCMPA11ALB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing68057
Missing (%)63.5%
Memory size837.0 KiB

NHCMP11L
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
81181 
1
25938 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 81181
75.8%
1 25938
 
24.2%

Length

2024-04-21T21:46:05.120954image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:05.216831image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 81181
75.8%
1 25938
 
24.2%

Most occurring characters

ValueCountFrequency (%)
2 81181
75.8%
1 25938
 
24.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 81181
75.8%
1 25938
 
24.2%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 81181
75.8%
1 25938
 
24.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 81181
75.8%
1 25938
 
24.2%

NHCMP11LA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing81181
Missing (%)75.8%
Memory size837.0 KiB

NHCMP11LB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing81181
Missing (%)75.8%
Memory size837.0 KiB

NHCMP11N
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
101848 
1
 
5271

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 101848
95.1%
1 5271
 
4.9%

Length

2024-04-21T21:46:05.321708image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:05.420025image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 101848
95.1%
1 5271
 
4.9%

Most occurring characters

ValueCountFrequency (%)
2 101848
95.1%
1 5271
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 101848
95.1%
1 5271
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 101848
95.1%
1 5271
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 101848
95.1%
1 5271
 
4.9%

NHCMP11NA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing101848
Missing (%)95.1%
Memory size837.0 KiB

NHCMP11NB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing101848
Missing (%)95.1%
Memory size837.0 KiB

NHCMP11O
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
101394 
1
 
5725

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 101394
94.7%
1 5725
 
5.3%

Length

2024-04-21T21:46:05.526850image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:05.623883image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 101394
94.7%
1 5725
 
5.3%

Most occurring characters

ValueCountFrequency (%)
2 101394
94.7%
1 5725
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 101394
94.7%
1 5725
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 101394
94.7%
1 5725
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 101394
94.7%
1 5725
 
5.3%

NHCMP11OA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing101394
Missing (%)94.7%
Memory size837.0 KiB

NHCMP11OB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing101394
Missing (%)94.7%
Memory size837.0 KiB

NHCMP11P
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
73013 
1
34106 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 73013
68.2%
1 34106
31.8%

Length

2024-04-21T21:46:05.728419image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:05.825765image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 73013
68.2%
1 34106
31.8%

Most occurring characters

ValueCountFrequency (%)
2 73013
68.2%
1 34106
31.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 73013
68.2%
1 34106
31.8%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 73013
68.2%
1 34106
31.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 73013
68.2%
1 34106
31.8%

NHCMP11PA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing73013
Missing (%)68.2%
Memory size837.0 KiB

NHCMP11PB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing73013
Missing (%)68.2%
Memory size837.0 KiB

NHCMP11Q
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
103778 
1
 
3341

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 103778
96.9%
1 3341
 
3.1%

Length

2024-04-21T21:46:05.931783image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:06.029868image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 103778
96.9%
1 3341
 
3.1%

Most occurring characters

ValueCountFrequency (%)
2 103778
96.9%
1 3341
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 103778
96.9%
1 3341
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 103778
96.9%
1 3341
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 103778
96.9%
1 3341
 
3.1%

NHCMP11QA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing103778
Missing (%)96.9%
Memory size837.0 KiB

NHCMP11QB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing103778
Missing (%)96.9%
Memory size837.0 KiB

NHCMP11R
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
97824 
1
 
9295

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 97824
91.3%
1 9295
 
8.7%

Length

2024-04-21T21:46:06.134487image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:06.231429image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 97824
91.3%
1 9295
 
8.7%

Most occurring characters

ValueCountFrequency (%)
2 97824
91.3%
1 9295
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 97824
91.3%
1 9295
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 97824
91.3%
1 9295
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 97824
91.3%
1 9295
 
8.7%

NHCMP11RA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing97824
Missing (%)91.3%
Memory size837.0 KiB

NHCMP11RB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing97824
Missing (%)91.3%
Memory size837.0 KiB

NHCMPA11R
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
82853 
1
24266 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 82853
77.3%
1 24266
 
22.7%

Length

2024-04-21T21:46:06.337520image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:06.435784image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 82853
77.3%
1 24266
 
22.7%

Most occurring characters

ValueCountFrequency (%)
2 82853
77.3%
1 24266
 
22.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 82853
77.3%
1 24266
 
22.7%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 82853
77.3%
1 24266
 
22.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 82853
77.3%
1 24266
 
22.7%

NHCMPA11RA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing82853
Missing (%)77.3%
Memory size837.0 KiB

NHCMPA11RB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing82853
Missing (%)77.3%
Memory size837.0 KiB

NHCMP11S
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
106619 
1
 
500

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 106619
99.5%
1 500
 
0.5%

Length

2024-04-21T21:46:06.543015image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:06.639977image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 106619
99.5%
1 500
 
0.5%

Most occurring characters

ValueCountFrequency (%)
2 106619
99.5%
1 500
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 106619
99.5%
1 500
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 106619
99.5%
1 500
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 106619
99.5%
1 500
 
0.5%

NHCMP11SA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing106619
Missing (%)99.5%
Memory size837.0 KiB

NHCMP11SB
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)4.2%
Missing106619
Missing (%)99.5%
Infinite0
Infinite (%)0.0%
Mean56602.768
Minimum0
Maximum2500000
Zeros454
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:46:06.742741image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile300000
Maximum2500000
Range2500000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation279829.12
Coefficient of variation (CV)4.9437356
Kurtosis37.765594
Mean56602.768
Median Absolute Deviation (MAD)0
Skewness5.957773
Sum28301384
Variance7.8304336 × 1010
MonotonicityNot monotonic
2024-04-21T21:46:06.869020image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 454
 
0.4%
99 12
 
< 0.1%
2000000 5
 
< 0.1%
1000000 4
 
< 0.1%
300000 3
 
< 0.1%
600000 3
 
< 0.1%
100000 2
 
< 0.1%
98 2
 
< 0.1%
1500000 2
 
< 0.1%
150000 2
 
< 0.1%
Other values (11) 11
 
< 0.1%
(Missing) 106619
99.5%
ValueCountFrequency (%)
0 454
0.4%
98 2
 
< 0.1%
99 12
 
< 0.1%
80000 1
 
< 0.1%
100000 2
 
< 0.1%
120000 1
 
< 0.1%
150000 2
 
< 0.1%
300000 3
 
< 0.1%
350000 1
 
< 0.1%
400000 1
 
< 0.1%
ValueCountFrequency (%)
2500000 1
 
< 0.1%
2000000 5
< 0.1%
1500000 2
 
< 0.1%
1400000 1
 
< 0.1%
1000000 4
< 0.1%
900000 1
 
< 0.1%
750000 1
 
< 0.1%
650000 1
 
< 0.1%
600000 3
< 0.1%
500000 1
 
< 0.1%

NHCMP11T
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
104827 
1
 
2292

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 104827
97.9%
1 2292
 
2.1%

Length

2024-04-21T21:46:06.992864image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:07.091120image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 104827
97.9%
1 2292
 
2.1%

Most occurring characters

ValueCountFrequency (%)
2 104827
97.9%
1 2292
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 104827
97.9%
1 2292
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 104827
97.9%
1 2292
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 104827
97.9%
1 2292
 
2.1%

NHCMP11TA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing104827
Missing (%)97.9%
Memory size837.0 KiB

NHCMP11TB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing104827
Missing (%)97.9%
Memory size837.0 KiB

NHCMP11U
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
99981 
1
 
7138

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 99981
93.3%
1 7138
 
6.7%

Length

2024-04-21T21:46:07.196567image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:07.293754image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 99981
93.3%
1 7138
 
6.7%

Most occurring characters

ValueCountFrequency (%)
2 99981
93.3%
1 7138
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 99981
93.3%
1 7138
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 99981
93.3%
1 7138
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 99981
93.3%
1 7138
 
6.7%

NHCMP11UA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing99981
Missing (%)93.3%
Memory size837.0 KiB

NHCMP11UB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing99981
Missing (%)93.3%
Memory size837.0 KiB

NHCMP11NU
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
104893 
1
 
2226

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 104893
97.9%
1 2226
 
2.1%

Length

2024-04-21T21:46:07.399708image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:07.497097image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 104893
97.9%
1 2226
 
2.1%

Most occurring characters

ValueCountFrequency (%)
2 104893
97.9%
1 2226
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 104893
97.9%
1 2226
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 104893
97.9%
1 2226
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 104893
97.9%
1 2226
 
2.1%

NHCMP11NUA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing104893
Missing (%)97.9%
Memory size837.0 KiB

NHCMP11NUB
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct50
Distinct (%)2.2%
Missing104893
Missing (%)97.9%
Infinite0
Infinite (%)0.0%
Mean94475.979
Minimum0
Maximum7000000
Zeros2003
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:46:07.617994image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile500000
Maximum7000000
Range7000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation475863.7
Coefficient of variation (CV)5.0368751
Kurtosis73.028425
Mean94475.979
Median Absolute Deviation (MAD)0
Skewness7.6042721
Sum2.1030353 × 108
Variance2.2644626 × 1011
MonotonicityNot monotonic
2024-04-21T21:46:07.776384image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2003
 
1.9%
99 41
 
< 0.1%
2000000 21
 
< 0.1%
98 15
 
< 0.1%
500000 13
 
< 0.1%
1000000 13
 
< 0.1%
800000 10
 
< 0.1%
200000 9
 
< 0.1%
300000 9
 
< 0.1%
1500000 6
 
< 0.1%
Other values (40) 86
 
0.1%
(Missing) 104893
97.9%
ValueCountFrequency (%)
0 2003
1.9%
98 15
 
< 0.1%
99 41
 
< 0.1%
1000 1
 
< 0.1%
20000 1
 
< 0.1%
50000 1
 
< 0.1%
70000 1
 
< 0.1%
80000 2
 
< 0.1%
90000 1
 
< 0.1%
100000 4
 
< 0.1%
ValueCountFrequency (%)
7000000 1
 
< 0.1%
6000000 2
 
< 0.1%
5700000 1
 
< 0.1%
5000000 1
 
< 0.1%
4700000 1
 
< 0.1%
4000000 4
< 0.1%
3000000 6
< 0.1%
2800000 1
 
< 0.1%
2600000 1
 
< 0.1%
2500000 2
 
< 0.1%

NHCMPA11U24
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing84274
Missing (%)78.7%
Memory size837.0 KiB
2.0
22193 
1.0
 
652

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters68535
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 22193
 
20.7%
1.0 652
 
0.6%
(Missing) 84274
78.7%

Length

2024-04-21T21:46:07.919566image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:08.015096image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 22193
97.1%
1.0 652
 
2.9%

Most occurring characters

ValueCountFrequency (%)
. 22845
33.3%
0 22845
33.3%
2 22193
32.4%
1 652
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45690
66.7%
Other Punctuation 22845
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22845
50.0%
2 22193
48.6%
1 652
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 22845
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 68535
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 22845
33.3%
0 22845
33.3%
2 22193
32.4%
1 652
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68535
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 22845
33.3%
0 22845
33.3%
2 22193
32.4%
1 652
 
1.0%

NHCMPA11UA24
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing106467
Missing (%)99.4%
Memory size837.0 KiB

NHCMPA11UB24
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)2.9%
Missing106467
Missing (%)99.4%
Infinite0
Infinite (%)0.0%
Mean75313.946
Minimum0
Maximum10000000
Zeros625
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:46:08.122640image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10000000
Range10000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation655706.5
Coefficient of variation (CV)8.7063091
Kurtosis149.60651
Mean75313.946
Median Absolute Deviation (MAD)0
Skewness11.590782
Sum49104693
Variance4.2995101 × 1011
MonotonicityNot monotonic
2024-04-21T21:46:08.259156image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 625
 
0.6%
99 7
 
< 0.1%
1000000 3
 
< 0.1%
2000000 2
 
< 0.1%
600000 1
 
< 0.1%
5000000 1
 
< 0.1%
6000000 1
 
< 0.1%
4000000 1
 
< 0.1%
30000 1
 
< 0.1%
1200000 1
 
< 0.1%
Other values (9) 9
 
< 0.1%
(Missing) 106467
99.4%
ValueCountFrequency (%)
0 625
0.6%
99 7
 
< 0.1%
20000 1
 
< 0.1%
24000 1
 
< 0.1%
30000 1
 
< 0.1%
120000 1
 
< 0.1%
150000 1
 
< 0.1%
600000 1
 
< 0.1%
1000000 3
 
< 0.1%
1200000 1
 
< 0.1%
ValueCountFrequency (%)
10000000 1
< 0.1%
9000000 1
< 0.1%
6000000 1
< 0.1%
5000000 1
< 0.1%
4000000 1
< 0.1%
2760000 1
< 0.1%
2000000 2
< 0.1%
1700000 1
< 0.1%
1500000 1
< 0.1%
1200000 1
< 0.1%

NHCMPB11U25
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing84274
Missing (%)78.7%
Memory size837.0 KiB
2.0
22502 
1.0
 
343

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters68535
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 22502
 
21.0%
1.0 343
 
0.3%
(Missing) 84274
78.7%

Length

2024-04-21T21:46:08.391212image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:08.488715image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 22502
98.5%
1.0 343
 
1.5%

Most occurring characters

ValueCountFrequency (%)
. 22845
33.3%
0 22845
33.3%
2 22502
32.8%
1 343
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45690
66.7%
Other Punctuation 22845
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22845
50.0%
2 22502
49.2%
1 343
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 22845
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 68535
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 22845
33.3%
0 22845
33.3%
2 22502
32.8%
1 343
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68535
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 22845
33.3%
0 22845
33.3%
2 22502
32.8%
1 343
 
0.5%

NHCMPB11UA25
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing106776
Missing (%)99.7%
Memory size837.0 KiB

NHCMPB11UB25
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing106776
Missing (%)99.7%
Memory size837.0 KiB

NHCMP12
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2
106306 
1
 
813

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters107119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 106306
99.2%
1 813
 
0.8%

Length

2024-04-21T21:46:08.594995image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:08.696765image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 106306
99.2%
1 813
 
0.8%

Most occurring characters

ValueCountFrequency (%)
2 106306
99.2%
1 813
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107119
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 106306
99.2%
1 813
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 107119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 106306
99.2%
1 813
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 106306
99.2%
1 813
 
0.8%

NHCMP12A_1
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.8%
Missing106989
Missing (%)99.9%
Memory size837.0 KiB
1.0
130 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters390
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 130
 
0.1%
(Missing) 106989
99.9%

Length

2024-04-21T21:46:08.805763image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:08.906414image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 130
100.0%

Most occurring characters

ValueCountFrequency (%)
1 130
33.3%
. 130
33.3%
0 130
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260
66.7%
Other Punctuation 130
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 130
50.0%
0 130
50.0%
Other Punctuation
ValueCountFrequency (%)
. 130
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 390
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 130
33.3%
. 130
33.3%
0 130
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 130
33.3%
. 130
33.3%
0 130
33.3%

NHCMP12A_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.6%
Missing106964
Missing (%)99.9%
Memory size837.0 KiB
1.0
155 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters465
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 155
 
0.1%
(Missing) 106964
99.9%

Length

2024-04-21T21:46:09.012685image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:09.115697image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 155
100.0%

Most occurring characters

ValueCountFrequency (%)
1 155
33.3%
. 155
33.3%
0 155
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 310
66.7%
Other Punctuation 155
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 155
50.0%
0 155
50.0%
Other Punctuation
ValueCountFrequency (%)
. 155
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 465
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 155
33.3%
. 155
33.3%
0 155
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 465
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 155
33.3%
. 155
33.3%
0 155
33.3%

NHCMP12A_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)6.2%
Missing107103
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
16 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters48
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 16
 
< 0.1%
(Missing) 107103
> 99.9%

Length

2024-04-21T21:46:09.227374image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:09.335723image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 16
100.0%

Most occurring characters

ValueCountFrequency (%)
1 16
33.3%
. 16
33.3%
0 16
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32
66.7%
Other Punctuation 16
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16
50.0%
0 16
50.0%
Other Punctuation
ValueCountFrequency (%)
. 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 16
33.3%
. 16
33.3%
0 16
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 16
33.3%
. 16
33.3%
0 16
33.3%

NHCMP12A_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)2.9%
Missing107084
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
35 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters105
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 35
 
< 0.1%
(Missing) 107084
> 99.9%

Length

2024-04-21T21:46:10.772950image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:10.878217image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 35
100.0%

Most occurring characters

ValueCountFrequency (%)
1 35
33.3%
. 35
33.3%
0 35
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70
66.7%
Other Punctuation 35
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 35
50.0%
0 35
50.0%
Other Punctuation
ValueCountFrequency (%)
. 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 105
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 35
33.3%
. 35
33.3%
0 35
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 105
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 35
33.3%
. 35
33.3%
0 35
33.3%

NHCMP12A_5
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)9.1%
Missing107108
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
11 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters33
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 11
 
< 0.1%
(Missing) 107108
> 99.9%

Length

2024-04-21T21:46:10.985690image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:11.082136image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 11
100.0%

Most occurring characters

ValueCountFrequency (%)
1 11
33.3%
. 11
33.3%
0 11
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22
66.7%
Other Punctuation 11
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11
50.0%
0 11
50.0%
Other Punctuation
ValueCountFrequency (%)
. 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 11
33.3%
. 11
33.3%
0 11
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11
33.3%
. 11
33.3%
0 11
33.3%

NHCMP12A_6
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)14.3%
Missing107112
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 7
 
< 0.1%
(Missing) 107112
> 99.9%

Length

2024-04-21T21:46:11.188560image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:11.293373image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7
100.0%

Most occurring characters

ValueCountFrequency (%)
1 7
33.3%
. 7
33.3%
0 7
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14
66.7%
Other Punctuation 7
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7
50.0%
0 7
50.0%
Other Punctuation
ValueCountFrequency (%)
. 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7
33.3%
. 7
33.3%
0 7
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7
33.3%
. 7
33.3%
0 7
33.3%

NHCMP12A_7
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)10.0%
Missing107109
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
10 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 10
 
< 0.1%
(Missing) 107109
> 99.9%

Length

2024-04-21T21:46:11.400572image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:11.496337image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 10
100.0%

Most occurring characters

ValueCountFrequency (%)
1 10
33.3%
. 10
33.3%
0 10
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20
66.7%
Other Punctuation 10
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10
50.0%
0 10
50.0%
Other Punctuation
ValueCountFrequency (%)
. 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10
33.3%
. 10
33.3%
0 10
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10
33.3%
. 10
33.3%
0 10
33.3%

NHCMP12A_8
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.6%
Missing107055
Missing (%)99.9%
Memory size837.0 KiB
1.0
64 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters192
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 64
 
0.1%
(Missing) 107055
99.9%

Length

2024-04-21T21:46:11.602604image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:11.706311image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 64
100.0%

Most occurring characters

ValueCountFrequency (%)
1 64
33.3%
. 64
33.3%
0 64
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 128
66.7%
Other Punctuation 64
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 64
50.0%
0 64
50.0%
Other Punctuation
ValueCountFrequency (%)
. 64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 64
33.3%
. 64
33.3%
0 64
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 64
33.3%
. 64
33.3%
0 64
33.3%

NHCMP12A_9
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.0%
Missing107023
Missing (%)99.9%
Memory size837.0 KiB
1.0
96 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters288
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 96
 
0.1%
(Missing) 107023
99.9%

Length

2024-04-21T21:46:11.813016image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:11.906985image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 96
100.0%

Most occurring characters

ValueCountFrequency (%)
1 96
33.3%
. 96
33.3%
0 96
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 192
66.7%
Other Punctuation 96
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 96
50.0%
0 96
50.0%
Other Punctuation
ValueCountFrequency (%)
. 96
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 96
33.3%
. 96
33.3%
0 96
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 96
33.3%
. 96
33.3%
0 96
33.3%

NHCMP12A_10
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.0%
Missing107016
Missing (%)99.9%
Memory size837.0 KiB
1.0
103 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters309
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 103
 
0.1%
(Missing) 107016
99.9%

Length

2024-04-21T21:46:12.009573image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:12.106936image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 103
100.0%

Most occurring characters

ValueCountFrequency (%)
1 103
33.3%
. 103
33.3%
0 103
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 206
66.7%
Other Punctuation 103
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 103
50.0%
0 103
50.0%
Other Punctuation
ValueCountFrequency (%)
. 103
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 309
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 103
33.3%
. 103
33.3%
0 103
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 309
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 103
33.3%
. 103
33.3%
0 103
33.3%

NHCMP12A_11
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.6%
Missing107055
Missing (%)99.9%
Memory size837.0 KiB
1.0
64 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters192
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 64
 
0.1%
(Missing) 107055
99.9%

Length

2024-04-21T21:46:12.214452image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:12.315819image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 64
100.0%

Most occurring characters

ValueCountFrequency (%)
1 64
33.3%
. 64
33.3%
0 64
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 128
66.7%
Other Punctuation 64
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 64
50.0%
0 64
50.0%
Other Punctuation
ValueCountFrequency (%)
. 64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 64
33.3%
. 64
33.3%
0 64
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 64
33.3%
. 64
33.3%
0 64
33.3%

NHCMP12A_12
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.8%
Missing106999
Missing (%)99.9%
Memory size837.0 KiB
1.0
120 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 120
 
0.1%
(Missing) 106999
99.9%

Length

2024-04-21T21:46:12.423546image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:12.523493image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 120
100.0%

Most occurring characters

ValueCountFrequency (%)
1 120
33.3%
. 120
33.3%
0 120
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 240
66.7%
Other Punctuation 120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 120
50.0%
0 120
50.0%
Other Punctuation
ValueCountFrequency (%)
. 120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 120
33.3%
. 120
33.3%
0 120
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 120
33.3%
. 120
33.3%
0 120
33.3%

NHCMP12A_13
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)2.9%
Missing107084
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
35 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters105
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 35
 
< 0.1%
(Missing) 107084
> 99.9%

Length

2024-04-21T21:46:12.632297image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:12.740196image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 35
100.0%

Most occurring characters

ValueCountFrequency (%)
1 35
33.3%
. 35
33.3%
0 35
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70
66.7%
Other Punctuation 35
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 35
50.0%
0 35
50.0%
Other Punctuation
ValueCountFrequency (%)
. 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 105
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 35
33.3%
. 35
33.3%
0 35
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 105
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 35
33.3%
. 35
33.3%
0 35
33.3%

NHCMP12A_14
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)11.1%
Missing107110
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters27
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 9
 
< 0.1%
(Missing) 107110
> 99.9%

Length

2024-04-21T21:46:12.846079image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:12.951784image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 9
100.0%

Most occurring characters

ValueCountFrequency (%)
1 9
33.3%
. 9
33.3%
0 9
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18
66.7%
Other Punctuation 9
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9
50.0%
0 9
50.0%
Other Punctuation
ValueCountFrequency (%)
. 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9
33.3%
. 9
33.3%
0 9
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9
33.3%
. 9
33.3%
0 9
33.3%

NHCMP12A_15
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)2.3%
Missing107075
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
44 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters132
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 44
 
< 0.1%
(Missing) 107075
> 99.9%

Length

2024-04-21T21:46:13.061476image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:13.165347image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 44
100.0%

Most occurring characters

ValueCountFrequency (%)
1 44
33.3%
. 44
33.3%
0 44
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 88
66.7%
Other Punctuation 44
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 44
50.0%
0 44
50.0%
Other Punctuation
ValueCountFrequency (%)
. 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 132
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 44
33.3%
. 44
33.3%
0 44
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 44
33.3%
. 44
33.3%
0 44
33.3%

NHCMP12A_16
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.6%
Missing107058
Missing (%)99.9%
Memory size837.0 KiB
1.0
61 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters183
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 61
 
0.1%
(Missing) 107058
99.9%

Length

2024-04-21T21:46:13.278438image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:13.378504image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 61
100.0%

Most occurring characters

ValueCountFrequency (%)
1 61
33.3%
. 61
33.3%
0 61
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 122
66.7%
Other Punctuation 61
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 61
50.0%
0 61
50.0%
Other Punctuation
ValueCountFrequency (%)
. 61
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 183
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 61
33.3%
. 61
33.3%
0 61
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 183
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 61
33.3%
. 61
33.3%
0 61
33.3%

NHCMP12A_17
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.7%
Missing106982
Missing (%)99.9%
Memory size837.0 KiB
1.0
137 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters411
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 137
 
0.1%
(Missing) 106982
99.9%

Length

2024-04-21T21:46:13.491144image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:13.593700image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 137
100.0%

Most occurring characters

ValueCountFrequency (%)
1 137
33.3%
. 137
33.3%
0 137
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 274
66.7%
Other Punctuation 137
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 137
50.0%
0 137
50.0%
Other Punctuation
ValueCountFrequency (%)
. 137
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 411
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 137
33.3%
. 137
33.3%
0 137
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 411
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 137
33.3%
. 137
33.3%
0 137
33.3%

NHCMP12A_18
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.8%
Missing107093
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
26 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters78
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 26
 
< 0.1%
(Missing) 107093
> 99.9%

Length

2024-04-21T21:46:13.698656image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:13.797837image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 26
100.0%

Most occurring characters

ValueCountFrequency (%)
1 26
33.3%
. 26
33.3%
0 26
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52
66.7%
Other Punctuation 26
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 26
50.0%
0 26
50.0%
Other Punctuation
ValueCountFrequency (%)
. 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 78
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 26
33.3%
. 26
33.3%
0 26
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 26
33.3%
. 26
33.3%
0 26
33.3%

NHCMP12A_19
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)2.3%
Missing107075
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
44 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters132
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 44
 
< 0.1%
(Missing) 107075
> 99.9%

Length

2024-04-21T21:46:13.902882image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:14.008892image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 44
100.0%

Most occurring characters

ValueCountFrequency (%)
1 44
33.3%
. 44
33.3%
0 44
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 88
66.7%
Other Punctuation 44
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 44
50.0%
0 44
50.0%
Other Punctuation
ValueCountFrequency (%)
. 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 132
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 44
33.3%
. 44
33.3%
0 44
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 44
33.3%
. 44
33.3%
0 44
33.3%

NHCMP12A_20
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)4.2%
Missing107095
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
24 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters72
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 24
 
< 0.1%
(Missing) 107095
> 99.9%

Length

2024-04-21T21:46:14.120574image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:14.215886image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 24
100.0%

Most occurring characters

ValueCountFrequency (%)
1 24
33.3%
. 24
33.3%
0 24
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48
66.7%
Other Punctuation 24
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 24
50.0%
0 24
50.0%
Other Punctuation
ValueCountFrequency (%)
. 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 24
33.3%
. 24
33.3%
0 24
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 24
33.3%
. 24
33.3%
0 24
33.3%

NHCMP12A_21
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)8.3%
Missing107107
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
12 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 12
 
< 0.1%
(Missing) 107107
> 99.9%

Length

2024-04-21T21:46:14.319481image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:14.418661image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 12
100.0%

Most occurring characters

ValueCountFrequency (%)
1 12
33.3%
. 12
33.3%
0 12
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24
66.7%
Other Punctuation 12
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12
50.0%
0 12
50.0%
Other Punctuation
ValueCountFrequency (%)
. 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12
33.3%
. 12
33.3%
0 12
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12
33.3%
. 12
33.3%
0 12
33.3%

NHCMP12A_22
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)20.0%
Missing107114
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5
 
< 0.1%
(Missing) 107114
> 99.9%

Length

2024-04-21T21:46:14.527407image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:14.625025image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5
100.0%

Most occurring characters

ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
66.7%
Other Punctuation 5
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5
50.0%
0 5
50.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

NHCMP12A_23
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)2.9%
Missing107084
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
35 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters105
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 35
 
< 0.1%
(Missing) 107084
> 99.9%

Length

2024-04-21T21:46:14.732914image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:14.837412image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 35
100.0%

Most occurring characters

ValueCountFrequency (%)
1 35
33.3%
. 35
33.3%
0 35
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70
66.7%
Other Punctuation 35
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 35
50.0%
0 35
50.0%
Other Punctuation
ValueCountFrequency (%)
. 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 105
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 35
33.3%
. 35
33.3%
0 35
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 105
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 35
33.3%
. 35
33.3%
0 35
33.3%

NHCMP12A_24
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:14.945841image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:15.043970image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP12A_25
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12AAA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)29.2%
Missing106989
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean534745.97
Minimum98
Maximum7000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:46:15.156617image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile99
Q150000
median200000
Q3600000
95-th percentile2000000
Maximum7000000
Range6999902
Interquartile range (IQR)550000

Descriptive statistics

Standard deviation876412.27
Coefficient of variation (CV)1.638932
Kurtosis22.839598
Mean534745.97
Median Absolute Deviation (MAD)199901
Skewness3.8793976
Sum69516976
Variance7.6809847 × 1011
MonotonicityNot monotonic
2024-04-21T21:46:15.312008image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
99 16
 
< 0.1%
100000 12
 
< 0.1%
200000 9
 
< 0.1%
1000000 8
 
< 0.1%
300000 8
 
< 0.1%
2000000 8
 
< 0.1%
50000 6
 
< 0.1%
500000 6
 
< 0.1%
250000 5
 
< 0.1%
1200000 4
 
< 0.1%
Other values (28) 48
 
< 0.1%
(Missing) 106989
99.9%
ValueCountFrequency (%)
98 4
 
< 0.1%
99 16
< 0.1%
20000 3
 
< 0.1%
25000 2
 
< 0.1%
30000 4
 
< 0.1%
50000 6
 
< 0.1%
55000 1
 
< 0.1%
60000 3
 
< 0.1%
65000 1
 
< 0.1%
70000 1
 
< 0.1%
ValueCountFrequency (%)
7000000 1
 
< 0.1%
3000000 2
 
< 0.1%
2500000 1
 
< 0.1%
2000000 8
< 0.1%
1800000 1
 
< 0.1%
1500000 2
 
< 0.1%
1300000 1
 
< 0.1%
1200000 4
< 0.1%
1000000 8
< 0.1%
850000 1
 
< 0.1%

NHCMP12AAB_1
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing107117
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0

Common Values

ValueCountFrequency (%)
1.0 2
 
< 0.1%
(Missing) 107117
> 99.9%

Length

2024-04-21T21:46:15.457950image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:15.564431image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
66.7%
Other Punctuation 2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2
50.0%
0 2
50.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

NHCMP12AAB_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)20.0%
Missing107114
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5
 
< 0.1%
(Missing) 107114
> 99.9%

Length

2024-04-21T21:46:15.675573image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:15.773649image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5
100.0%

Most occurring characters

ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
66.7%
Other Punctuation 5
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5
50.0%
0 5
50.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

NHCMP12AAB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.8%
Missing106998
Missing (%)99.9%
Memory size837.0 KiB
1.0
121 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters363
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 121
 
0.1%
(Missing) 106998
99.9%

Length

2024-04-21T21:46:15.881697image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:15.985192image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 121
100.0%

Most occurring characters

ValueCountFrequency (%)
1 121
33.3%
. 121
33.3%
0 121
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 242
66.7%
Other Punctuation 121
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 121
50.0%
0 121
50.0%
Other Punctuation
ValueCountFrequency (%)
. 121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 363
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 121
33.3%
. 121
33.3%
0 121
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 363
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 121
33.3%
. 121
33.3%
0 121
33.3%

NHCMP12AAB_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)20.0%
Missing107114
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5
 
< 0.1%
(Missing) 107114
> 99.9%

Length

2024-04-21T21:46:16.094952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:16.191487image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5
100.0%

Most occurring characters

ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
66.7%
Other Punctuation 5
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5
50.0%
0 5
50.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

NHCMP12ABA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct47
Distinct (%)30.3%
Missing106964
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean517942.91
Minimum98
Maximum8000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:46:16.313766image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile99
Q135000
median200000
Q3800000
95-th percentile2000000
Maximum8000000
Range7999902
Interquartile range (IQR)765000

Descriptive statistics

Standard deviation870037.12
Coefficient of variation (CV)1.6797935
Kurtosis35.940414
Mean517942.91
Median Absolute Deviation (MAD)199901
Skewness4.8814317
Sum80281151
Variance7.5696459 × 1011
MonotonicityNot monotonic
2024-04-21T21:46:16.481873image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
99 25
 
< 0.1%
200000 10
 
< 0.1%
1000000 8
 
< 0.1%
50000 7
 
< 0.1%
800000 7
 
< 0.1%
100000 6
 
< 0.1%
500000 6
 
< 0.1%
98 6
 
< 0.1%
400000 5
 
< 0.1%
250000 5
 
< 0.1%
Other values (37) 70
 
0.1%
(Missing) 106964
99.9%
ValueCountFrequency (%)
98 6
 
< 0.1%
99 25
< 0.1%
20000 4
 
< 0.1%
25000 2
 
< 0.1%
30000 2
 
< 0.1%
40000 1
 
< 0.1%
45000 1
 
< 0.1%
50000 7
 
< 0.1%
58000 1
 
< 0.1%
60000 3
 
< 0.1%
ValueCountFrequency (%)
8000000 1
 
< 0.1%
3500000 1
 
< 0.1%
3000000 1
 
< 0.1%
2500000 1
 
< 0.1%
2300000 1
 
< 0.1%
2000000 5
< 0.1%
1700000 1
 
< 0.1%
1500000 1
 
< 0.1%
1300000 2
 
< 0.1%
1200000 4
< 0.1%

NHCMP12ABB_1
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:16.630635image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:16.728220image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP12ABB_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)16.7%
Missing107113
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 6
 
< 0.1%
(Missing) 107113
> 99.9%

Length

2024-04-21T21:46:16.835659image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:16.937879image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 6
100.0%

Most occurring characters

ValueCountFrequency (%)
1 6
33.3%
. 6
33.3%
0 6
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12
66.7%
Other Punctuation 6
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6
50.0%
0 6
50.0%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6
33.3%
. 6
33.3%
0 6
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6
33.3%
. 6
33.3%
0 6
33.3%

NHCMP12ABB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.7%
Missing106972
Missing (%)99.9%
Memory size837.0 KiB
1.0
147 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters441
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 147
 
0.1%
(Missing) 106972
99.9%

Length

2024-04-21T21:46:17.049887image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:17.157146image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 147
100.0%

Most occurring characters

ValueCountFrequency (%)
1 147
33.3%
. 147
33.3%
0 147
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 294
66.7%
Other Punctuation 147
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 147
50.0%
0 147
50.0%
Other Punctuation
ValueCountFrequency (%)
. 147
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 147
33.3%
. 147
33.3%
0 147
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 147
33.3%
. 147
33.3%
0 147
33.3%

NHCMP12ABB_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)33.3%
Missing107116
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0

Common Values

ValueCountFrequency (%)
1.0 3
 
< 0.1%
(Missing) 107116
> 99.9%

Length

2024-04-21T21:46:17.266287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:17.367370image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3
100.0%

Most occurring characters

ValueCountFrequency (%)
1 3
33.3%
. 3
33.3%
0 3
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6
66.7%
Other Punctuation 3
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3
50.0%
0 3
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3
33.3%
. 3
33.3%
0 3
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3
33.3%
. 3
33.3%
0 3
33.3%

NHCMP12ACA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)56.2%
Missing107103
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean217849.62
Minimum99
Maximum2000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:46:17.463674image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum99
5-th percentile99
Q199
median50000
Q397500
95-th percentile1175000
Maximum2000000
Range1999901
Interquartile range (IQR)97401

Descriptive statistics

Standard deviation522895.13
Coefficient of variation (CV)2.4002572
Kurtosis10.047598
Mean217849.62
Median Absolute Deviation (MAD)49901
Skewness3.1378698
Sum3485594
Variance2.7341931 × 1011
MonotonicityNot monotonic
2024-04-21T21:46:17.596752image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
99 6
 
< 0.1%
50000 2
 
< 0.1%
120000 2
 
< 0.1%
90000 1
 
< 0.1%
30000 1
 
< 0.1%
65000 1
 
< 0.1%
2000000 1
 
< 0.1%
900000 1
 
< 0.1%
60000 1
 
< 0.1%
(Missing) 107103
> 99.9%
ValueCountFrequency (%)
99 6
< 0.1%
30000 1
 
< 0.1%
50000 2
 
< 0.1%
60000 1
 
< 0.1%
65000 1
 
< 0.1%
90000 1
 
< 0.1%
120000 2
 
< 0.1%
900000 1
 
< 0.1%
2000000 1
 
< 0.1%
ValueCountFrequency (%)
2000000 1
 
< 0.1%
900000 1
 
< 0.1%
120000 2
 
< 0.1%
90000 1
 
< 0.1%
65000 1
 
< 0.1%
60000 1
 
< 0.1%
50000 2
 
< 0.1%
30000 1
 
< 0.1%
99 6
< 0.1%

NHCMP12ACB_1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12ACB_2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12ACB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)6.7%
Missing107104
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
15 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 15
 
< 0.1%
(Missing) 107104
> 99.9%

Length

2024-04-21T21:46:17.739732image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:17.844971image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 15
100.0%

Most occurring characters

ValueCountFrequency (%)
1 15
33.3%
. 15
33.3%
0 15
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30
66.7%
Other Punctuation 15
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 15
50.0%
0 15
50.0%
Other Punctuation
ValueCountFrequency (%)
. 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 15
33.3%
. 15
33.3%
0 15
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 15
33.3%
. 15
33.3%
0 15
33.3%

NHCMP12ACB_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:17.952727image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:18.049408image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP12ADA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)65.7%
Missing107084
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean1051448.3
Minimum98
Maximum8000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:46:18.153334image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile99
Q165000
median500000
Q31150000
95-th percentile4200000
Maximum8000000
Range7999902
Interquartile range (IQR)1085000

Descriptive statistics

Standard deviation1775840.9
Coefficient of variation (CV)1.6889474
Kurtosis9.4957799
Mean1051448.3
Median Absolute Deviation (MAD)499901
Skewness3.0296771
Sum36800692
Variance3.153611 × 1012
MonotonicityNot monotonic
2024-04-21T21:46:18.302237image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
99 6
 
< 0.1%
300000 3
 
< 0.1%
150000 2
 
< 0.1%
1500000 2
 
< 0.1%
1000000 2
 
< 0.1%
500000 2
 
< 0.1%
600000 2
 
< 0.1%
350000 1
 
< 0.1%
50000 1
 
< 0.1%
20000 1
 
< 0.1%
Other values (13) 13
 
< 0.1%
(Missing) 107084
> 99.9%
ValueCountFrequency (%)
98 1
 
< 0.1%
99 6
< 0.1%
20000 1
 
< 0.1%
50000 1
 
< 0.1%
80000 1
 
< 0.1%
150000 2
 
< 0.1%
300000 3
< 0.1%
350000 1
 
< 0.1%
400000 1
 
< 0.1%
500000 2
 
< 0.1%
ValueCountFrequency (%)
8000000 1
< 0.1%
7000000 1
< 0.1%
3000000 1
< 0.1%
2800000 1
< 0.1%
1500000 2
< 0.1%
1400000 1
< 0.1%
1300000 1
< 0.1%
1200000 1
< 0.1%
1100000 1
< 0.1%
1000000 2
< 0.1%

NHCMP12ADB_1
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing107117
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0

Common Values

ValueCountFrequency (%)
1.0 2
 
< 0.1%
(Missing) 107117
> 99.9%

Length

2024-04-21T21:46:18.446609image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:18.550133image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
66.7%
Other Punctuation 2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2
50.0%
0 2
50.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

NHCMP12ADB_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing107117
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0

Common Values

ValueCountFrequency (%)
1.0 2
 
< 0.1%
(Missing) 107117
> 99.9%

Length

2024-04-21T21:46:18.659036image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:18.764550image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
66.7%
Other Punctuation 2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2
50.0%
0 2
50.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

NHCMP12ADB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.2%
Missing107088
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
31 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters93
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 31
 
< 0.1%
(Missing) 107088
> 99.9%

Length

2024-04-21T21:46:18.877284image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:18.977432image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 31
100.0%

Most occurring characters

ValueCountFrequency (%)
1 31
33.3%
. 31
33.3%
0 31
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62
66.7%
Other Punctuation 31
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 31
50.0%
0 31
50.0%
Other Punctuation
ValueCountFrequency (%)
. 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 93
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 31
33.3%
. 31
33.3%
0 31
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 31
33.3%
. 31
33.3%
0 31
33.3%

NHCMP12ADB_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:19.082688image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:19.179917image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP12AEA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107108
Missing (%)> 99.9%
Memory size837.0 KiB

NHCMP12AEB_1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12AEB_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:19.284386image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:19.380400image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP12AEB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)10.0%
Missing107109
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
10 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 10
 
< 0.1%
(Missing) 107109
> 99.9%

Length

2024-04-21T21:46:19.483836image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:19.579152image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 10
100.0%

Most occurring characters

ValueCountFrequency (%)
1 10
33.3%
. 10
33.3%
0 10
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20
66.7%
Other Punctuation 10
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10
50.0%
0 10
50.0%
Other Punctuation
ValueCountFrequency (%)
. 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10
33.3%
. 10
33.3%
0 10
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10
33.3%
. 10
33.3%
0 10
33.3%

NHCMP12AEB_4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12AFA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)100.0%
Missing107112
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean280728.43
Minimum99
Maximum1500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:46:19.668505image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum99
5-th percentile6069.3
Q122500
median68000
Q3176000
95-th percentile1125000
Maximum1500000
Range1499901
Interquartile range (IQR)153500

Descriptive statistics

Standard deviation544195.71
Coefficient of variation (CV)1.938513
Kurtosis6.456156
Mean280728.43
Median Absolute Deviation (MAD)48000
Skewness2.5201789
Sum1965099
Variance2.9614898 × 1011
MonotonicityNot monotonic
2024-04-21T21:46:19.789233image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
250000 1
 
< 0.1%
1500000 1
 
< 0.1%
20000 1
 
< 0.1%
102000 1
 
< 0.1%
68000 1
 
< 0.1%
25000 1
 
< 0.1%
99 1
 
< 0.1%
(Missing) 107112
> 99.9%
ValueCountFrequency (%)
99 1
< 0.1%
20000 1
< 0.1%
25000 1
< 0.1%
68000 1
< 0.1%
102000 1
< 0.1%
250000 1
< 0.1%
1500000 1
< 0.1%
ValueCountFrequency (%)
1500000 1
< 0.1%
250000 1
< 0.1%
102000 1
< 0.1%
68000 1
< 0.1%
25000 1
< 0.1%
20000 1
< 0.1%
99 1
< 0.1%

NHCMP12AFB_1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12AFB_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:19.927894image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:20.028264image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP12AFB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)16.7%
Missing107113
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 6
 
< 0.1%
(Missing) 107113
> 99.9%

Length

2024-04-21T21:46:20.136917image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:20.239444image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 6
100.0%

Most occurring characters

ValueCountFrequency (%)
1 6
33.3%
. 6
33.3%
0 6
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12
66.7%
Other Punctuation 6
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6
50.0%
0 6
50.0%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6
33.3%
. 6
33.3%
0 6
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6
33.3%
. 6
33.3%
0 6
33.3%

NHCMP12AFB_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing107117
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0

Common Values

ValueCountFrequency (%)
1.0 2
 
< 0.1%
(Missing) 107117
> 99.9%

Length

2024-04-21T21:46:20.347288image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:20.455676image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
66.7%
Other Punctuation 2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2
50.0%
0 2
50.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

NHCMP12AGA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)70.0%
Missing107109
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean316029.7
Minimum99
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:46:20.562762image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum99
5-th percentile99
Q115074.25
median250000
Q3500000
95-th percentile820000
Maximum1000000
Range999901
Interquartile range (IQR)484925.75

Descriptive statistics

Standard deviation332474.28
Coefficient of variation (CV)1.0520349
Kurtosis0.34074558
Mean316029.7
Median Absolute Deviation (MAD)249901
Skewness0.91596386
Sum3160297
Variance1.1053915 × 1011
MonotonicityNot monotonic
2024-04-21T21:46:20.686063image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
99 3
 
< 0.1%
500000 2
 
< 0.1%
1000000 1
 
< 0.1%
600000 1
 
< 0.1%
300000 1
 
< 0.1%
60000 1
 
< 0.1%
200000 1
 
< 0.1%
(Missing) 107109
> 99.9%
ValueCountFrequency (%)
99 3
< 0.1%
60000 1
 
< 0.1%
200000 1
 
< 0.1%
300000 1
 
< 0.1%
500000 2
< 0.1%
600000 1
 
< 0.1%
1000000 1
 
< 0.1%
ValueCountFrequency (%)
1000000 1
 
< 0.1%
600000 1
 
< 0.1%
500000 2
< 0.1%
300000 1
 
< 0.1%
200000 1
 
< 0.1%
60000 1
 
< 0.1%
99 3
< 0.1%

NHCMP12AGB_1
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing107117
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0

Common Values

ValueCountFrequency (%)
1.0 2
 
< 0.1%
(Missing) 107117
> 99.9%

Length

2024-04-21T21:46:20.816623image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:20.923130image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
66.7%
Other Punctuation 2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2
50.0%
0 2
50.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

NHCMP12AGB_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:21.038248image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:21.138779image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP12AGB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)14.3%
Missing107112
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 7
 
< 0.1%
(Missing) 107112
> 99.9%

Length

2024-04-21T21:46:21.248208image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:21.357059image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7
100.0%

Most occurring characters

ValueCountFrequency (%)
1 7
33.3%
. 7
33.3%
0 7
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14
66.7%
Other Punctuation 7
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7
50.0%
0 7
50.0%
Other Punctuation
ValueCountFrequency (%)
. 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7
33.3%
. 7
33.3%
0 7
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7
33.3%
. 7
33.3%
0 7
33.3%

NHCMP12AGB_4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12AHA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)34.4%
Missing107055
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean105074.86
Minimum98
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:46:21.472185image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile99
Q127500
median67500
Q3150000
95-th percentile288000
Maximum1000000
Range999902
Interquartile range (IQR)122500

Descriptive statistics

Standard deviation142898.1
Coefficient of variation (CV)1.3599647
Kurtosis24.523036
Mean105074.86
Median Absolute Deviation (MAD)47500
Skewness4.2839989
Sum6724791
Variance2.0419867 × 1010
MonotonicityNot monotonic
2024-04-21T21:46:21.612463image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
50000 8
 
< 0.1%
99 7
 
< 0.1%
200000 7
 
< 0.1%
100000 6
 
< 0.1%
150000 5
 
< 0.1%
80000 5
 
< 0.1%
20000 5
 
< 0.1%
60000 3
 
< 0.1%
70000 2
 
< 0.1%
52000 2
 
< 0.1%
Other values (12) 14
 
< 0.1%
(Missing) 107055
99.9%
ValueCountFrequency (%)
98 1
 
< 0.1%
99 7
< 0.1%
10000 2
 
< 0.1%
15000 1
 
< 0.1%
20000 5
< 0.1%
30000 1
 
< 0.1%
40000 1
 
< 0.1%
50000 8
< 0.1%
52000 2
 
< 0.1%
60000 3
 
< 0.1%
ValueCountFrequency (%)
1000000 1
 
< 0.1%
450000 1
 
< 0.1%
300000 2
 
< 0.1%
220000 1
 
< 0.1%
200000 7
< 0.1%
150000 5
< 0.1%
120000 1
 
< 0.1%
100000 6
< 0.1%
90000 1
 
< 0.1%
80000 5
< 0.1%

NHCMP12AHB_1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12AHB_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:21.750385image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:21.854028image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP12AHB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.6%
Missing107056
Missing (%)99.9%
Memory size837.0 KiB
1.0
63 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters189
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 63
 
0.1%
(Missing) 107056
99.9%

Length

2024-04-21T21:46:21.962929image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:22.067351image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 63
100.0%

Most occurring characters

ValueCountFrequency (%)
1 63
33.3%
. 63
33.3%
0 63
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 126
66.7%
Other Punctuation 63
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 63
50.0%
0 63
50.0%
Other Punctuation
ValueCountFrequency (%)
. 63
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 189
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 63
33.3%
. 63
33.3%
0 63
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 189
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 63
33.3%
. 63
33.3%
0 63
33.3%

NHCMP12AHB_4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12AIA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)27.1%
Missing107023
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean237715.32
Minimum98
Maximum5000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:46:22.179157image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile99
Q199
median50000
Q3173250
95-th percentile1125000
Maximum5000000
Range4999902
Interquartile range (IQR)173151

Descriptive statistics

Standard deviation653922.31
Coefficient of variation (CV)2.7508631
Kurtosis32.072358
Mean237715.32
Median Absolute Deviation (MAD)49901
Skewness5.227926
Sum22820671
Variance4.2761439 × 1011
MonotonicityNot monotonic
2024-04-21T21:46:22.323529image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
99 25
 
< 0.1%
50000 13
 
< 0.1%
100000 11
 
< 0.1%
200000 8
 
< 0.1%
80000 4
 
< 0.1%
120000 3
 
< 0.1%
10000 3
 
< 0.1%
300000 3
 
< 0.1%
60000 3
 
< 0.1%
2000000 2
 
< 0.1%
Other values (16) 21
 
< 0.1%
(Missing) 107023
99.9%
ValueCountFrequency (%)
98 2
 
< 0.1%
99 25
< 0.1%
10000 3
 
< 0.1%
20000 2
 
< 0.1%
25000 1
 
< 0.1%
30000 2
 
< 0.1%
32000 1
 
< 0.1%
50000 13
< 0.1%
60000 3
 
< 0.1%
70000 1
 
< 0.1%
ValueCountFrequency (%)
5000000 1
 
< 0.1%
2700000 1
 
< 0.1%
2000000 2
< 0.1%
1500000 1
 
< 0.1%
1000000 1
 
< 0.1%
800000 1
 
< 0.1%
600000 1
 
< 0.1%
500000 2
< 0.1%
300000 3
< 0.1%
250000 2
< 0.1%

NHCMP12AIB_1
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:22.456168image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:22.555952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP12AIB_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing107117
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0

Common Values

ValueCountFrequency (%)
1.0 2
 
< 0.1%
(Missing) 107117
> 99.9%

Length

2024-04-21T21:46:22.665120image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:22.774735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
66.7%
Other Punctuation 2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2
50.0%
0 2
50.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

NHCMP12AIB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.1%
Missing107025
Missing (%)99.9%
Memory size837.0 KiB
1.0
94 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters282
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 94
 
0.1%
(Missing) 107025
99.9%

Length

2024-04-21T21:46:22.889364image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:22.986313image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 94
100.0%

Most occurring characters

ValueCountFrequency (%)
1 94
33.3%
. 94
33.3%
0 94
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 188
66.7%
Other Punctuation 94
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 94
50.0%
0 94
50.0%
Other Punctuation
ValueCountFrequency (%)
. 94
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 282
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 94
33.3%
. 94
33.3%
0 94
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 282
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 94
33.3%
. 94
33.3%
0 94
33.3%

NHCMP12AIB_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:23.092298image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:23.195322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP12AJA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)25.2%
Missing107016
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean100479.38
Minimum99
Maximum1500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:46:23.302747image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum99
5-th percentile99
Q112500
median50000
Q3100000
95-th percentile345000
Maximum1500000
Range1499901
Interquartile range (IQR)87500

Descriptive statistics

Standard deviation188531.49
Coefficient of variation (CV)1.8763202
Kurtosis31.185704
Mean100479.38
Median Absolute Deviation (MAD)49901
Skewness4.9020134
Sum10349376
Variance3.5544121 × 1010
MonotonicityNot monotonic
2024-04-21T21:46:23.452627image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
99 24
 
< 0.1%
50000 12
 
< 0.1%
100000 9
 
< 0.1%
30000 7
 
< 0.1%
40000 6
 
< 0.1%
200000 5
 
< 0.1%
60000 4
 
< 0.1%
150000 4
 
< 0.1%
20000 4
 
< 0.1%
25000 4
 
< 0.1%
Other values (16) 24
 
< 0.1%
(Missing) 107016
99.9%
ValueCountFrequency (%)
99 24
< 0.1%
10000 2
 
< 0.1%
15000 1
 
< 0.1%
20000 4
 
< 0.1%
25000 4
 
< 0.1%
30000 7
 
< 0.1%
32000 1
 
< 0.1%
35000 1
 
< 0.1%
40000 6
 
< 0.1%
45000 1
 
< 0.1%
ValueCountFrequency (%)
1500000 1
 
< 0.1%
800000 1
 
< 0.1%
500000 3
< 0.1%
350000 1
 
< 0.1%
300000 2
 
< 0.1%
250000 3
< 0.1%
200000 5
< 0.1%
180000 1
 
< 0.1%
150000 4
< 0.1%
130000 1
 
< 0.1%

NHCMP12AJB_1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12AJB_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)20.0%
Missing107114
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5
 
< 0.1%
(Missing) 107114
> 99.9%

Length

2024-04-21T21:46:23.604640image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:23.709403image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5
100.0%

Most occurring characters

ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
66.7%
Other Punctuation 5
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5
50.0%
0 5
50.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

NHCMP12AJB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.0%
Missing107020
Missing (%)99.9%
Memory size837.0 KiB
1.0
99 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters297
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 99
 
0.1%
(Missing) 107020
99.9%

Length

2024-04-21T21:46:23.819198image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:23.920567image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 99
100.0%

Most occurring characters

ValueCountFrequency (%)
1 99
33.3%
. 99
33.3%
0 99
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 198
66.7%
Other Punctuation 99
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 99
50.0%
0 99
50.0%
Other Punctuation
ValueCountFrequency (%)
. 99
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 99
33.3%
. 99
33.3%
0 99
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 99
33.3%
. 99
33.3%
0 99
33.3%

NHCMP12AJB_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:24.031001image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:24.133822image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP12AKA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107055
Missing (%)99.9%
Memory size837.0 KiB

NHCMP12AKB_1
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:24.245059image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:24.348366image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP12AKB_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:24.459422image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:24.564886image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP12AKB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.6%
Missing107057
Missing (%)99.9%
Memory size837.0 KiB
1.0
62 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters186
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 62
 
0.1%
(Missing) 107057
99.9%

Length

2024-04-21T21:46:24.677890image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:24.787330image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 62
100.0%

Most occurring characters

ValueCountFrequency (%)
1 62
33.3%
. 62
33.3%
0 62
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124
66.7%
Other Punctuation 62
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 62
50.0%
0 62
50.0%
Other Punctuation
ValueCountFrequency (%)
. 62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 186
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 62
33.3%
. 62
33.3%
0 62
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 62
33.3%
. 62
33.3%
0 62
33.3%

NHCMP12AKB_4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMPA12ALA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct52
Distinct (%)43.3%
Missing106999
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean556928.03
Minimum98
Maximum3500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:46:24.915464image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile99
Q199
median210000
Q3900000
95-th percentile2000000
Maximum3500000
Range3499902
Interquartile range (IQR)899901

Descriptive statistics

Standard deviation721545.92
Coefficient of variation (CV)1.295582
Kurtosis2.1104766
Mean556928.03
Median Absolute Deviation (MAD)209901
Skewness1.5309626
Sum66831363
Variance5.2062852 × 1011
MonotonicityNot monotonic
2024-04-21T21:46:25.088818image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 31
 
< 0.1%
1000000 5
 
< 0.1%
2000000 5
 
< 0.1%
1500000 4
 
< 0.1%
600000 3
 
< 0.1%
120000 3
 
< 0.1%
60000 3
 
< 0.1%
98 3
 
< 0.1%
250000 3
 
< 0.1%
700000 3
 
< 0.1%
Other values (42) 57
 
0.1%
(Missing) 106999
99.9%
ValueCountFrequency (%)
98 3
 
< 0.1%
99 31
< 0.1%
30000 1
 
< 0.1%
45000 2
 
< 0.1%
50000 2
 
< 0.1%
60000 3
 
< 0.1%
62000 1
 
< 0.1%
67000 1
 
< 0.1%
72000 1
 
< 0.1%
80000 1
 
< 0.1%
ValueCountFrequency (%)
3500000 1
 
< 0.1%
2800000 1
 
< 0.1%
2500000 1
 
< 0.1%
2000000 5
< 0.1%
1900000 2
 
< 0.1%
1800000 1
 
< 0.1%
1700000 1
 
< 0.1%
1600000 2
 
< 0.1%
1500000 4
< 0.1%
1400000 2
 
< 0.1%

NHCMPA12ALB_1
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:25.247705image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:25.352319image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMPA12ALB_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:25.464670image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:25.569542image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMPA12ALB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.8%
Missing107001
Missing (%)99.9%
Memory size837.0 KiB
1.0
118 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters354
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 118
 
0.1%
(Missing) 107001
99.9%

Length

2024-04-21T21:46:25.681790image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:25.785769image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 118
100.0%

Most occurring characters

ValueCountFrequency (%)
1 118
33.3%
. 118
33.3%
0 118
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 236
66.7%
Other Punctuation 118
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 118
50.0%
0 118
50.0%
Other Punctuation
ValueCountFrequency (%)
. 118
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 118
33.3%
. 118
33.3%
0 118
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 118
33.3%
. 118
33.3%
0 118
33.3%

NHCMPA12ALB_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing107117
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0

Common Values

ValueCountFrequency (%)
1.0 2
 
< 0.1%
(Missing) 107117
> 99.9%

Length

2024-04-21T21:46:25.894159image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:26.003320image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
66.7%
Other Punctuation 2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2
50.0%
0 2
50.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

NHCMP12ALA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)42.9%
Missing107084
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean373699.54
Minimum98
Maximum2000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:46:26.123268image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile99
Q199
median250000
Q3500000
95-th percentile1150000
Maximum2000000
Range1999902
Interquartile range (IQR)499901

Descriptive statistics

Standard deviation465637.04
Coefficient of variation (CV)1.2460198
Kurtosis3.5881719
Mean373699.54
Median Absolute Deviation (MAD)249901
Skewness1.707514
Sum13079484
Variance2.1681785 × 1011
MonotonicityNot monotonic
2024-04-21T21:46:26.270841image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
99 14
 
< 0.1%
500000 6
 
< 0.1%
800000 2
 
< 0.1%
250000 2
 
< 0.1%
98 1
 
< 0.1%
200000 1
 
< 0.1%
900000 1
 
< 0.1%
600000 1
 
< 0.1%
198000 1
 
< 0.1%
1000000 1
 
< 0.1%
Other values (5) 5
 
< 0.1%
(Missing) 107084
> 99.9%
ValueCountFrequency (%)
98 1
 
< 0.1%
99 14
< 0.1%
198000 1
 
< 0.1%
200000 1
 
< 0.1%
250000 2
 
< 0.1%
450000 1
 
< 0.1%
480000 1
 
< 0.1%
500000 6
< 0.1%
600000 1
 
< 0.1%
650000 1
 
< 0.1%
ValueCountFrequency (%)
2000000 1
 
< 0.1%
1500000 1
 
< 0.1%
1000000 1
 
< 0.1%
900000 1
 
< 0.1%
800000 2
 
< 0.1%
650000 1
 
< 0.1%
600000 1
 
< 0.1%
500000 6
< 0.1%
480000 1
 
< 0.1%
450000 1
 
< 0.1%

NHCMP12ALB_1
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)33.3%
Missing107116
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0

Common Values

ValueCountFrequency (%)
1.0 3
 
< 0.1%
(Missing) 107116
> 99.9%

Length

2024-04-21T21:46:26.424997image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:26.535721image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3
100.0%

Most occurring characters

ValueCountFrequency (%)
1 3
33.3%
. 3
33.3%
0 3
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6
66.7%
Other Punctuation 3
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3
50.0%
0 3
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3
33.3%
. 3
33.3%
0 3
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3
33.3%
. 3
33.3%
0 3
33.3%

NHCMP12ALB_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)33.3%
Missing107116
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0

Common Values

ValueCountFrequency (%)
1.0 3
 
< 0.1%
(Missing) 107116
> 99.9%

Length

2024-04-21T21:46:26.655063image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:26.767632image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3
100.0%

Most occurring characters

ValueCountFrequency (%)
1 3
33.3%
. 3
33.3%
0 3
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6
66.7%
Other Punctuation 3
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3
50.0%
0 3
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3
33.3%
. 3
33.3%
0 3
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3
33.3%
. 3
33.3%
0 3
33.3%

NHCMP12ALB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.6%
Missing107091
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
28 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters84
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 28
 
< 0.1%
(Missing) 107091
> 99.9%

Length

2024-04-21T21:46:26.885076image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:26.993925image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 28
100.0%

Most occurring characters

ValueCountFrequency (%)
1 28
33.3%
. 28
33.3%
0 28
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 56
66.7%
Other Punctuation 28
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 28
50.0%
0 28
50.0%
Other Punctuation
ValueCountFrequency (%)
. 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 84
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 28
33.3%
. 28
33.3%
0 28
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 28
33.3%
. 28
33.3%
0 28
33.3%

NHCMP12ALB_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:27.109658image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:27.219417image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP12ANA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)47.7%
Missing107075
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean794799.68
Minimum98
Maximum5000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:46:27.335478image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile99
Q199
median300000
Q31000000
95-th percentile2925000
Maximum5000000
Range4999902
Interquartile range (IQR)999901

Descriptive statistics

Standard deviation1203276.7
Coefficient of variation (CV)1.513937
Kurtosis5.3618765
Mean794799.68
Median Absolute Deviation (MAD)299901
Skewness2.2732162
Sum34971186
Variance1.4478747 × 1012
MonotonicityNot monotonic
2024-04-21T21:46:27.480134image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
99 10
 
< 0.1%
1000000 4
 
< 0.1%
2000000 4
 
< 0.1%
200000 3
 
< 0.1%
300000 3
 
< 0.1%
150000 2
 
< 0.1%
98 2
 
< 0.1%
500000 2
 
< 0.1%
5000000 2
 
< 0.1%
180000 1
 
< 0.1%
Other values (11) 11
 
< 0.1%
(Missing) 107075
> 99.9%
ValueCountFrequency (%)
98 2
 
< 0.1%
99 10
< 0.1%
30000 1
 
< 0.1%
60000 1
 
< 0.1%
120000 1
 
< 0.1%
150000 2
 
< 0.1%
180000 1
 
< 0.1%
200000 3
 
< 0.1%
300000 3
 
< 0.1%
350000 1
 
< 0.1%
ValueCountFrequency (%)
5000000 2
< 0.1%
3000000 1
 
< 0.1%
2500000 1
 
< 0.1%
2000000 4
< 0.1%
1430000 1
 
< 0.1%
1000000 4
< 0.1%
800000 1
 
< 0.1%
700000 1
 
< 0.1%
600000 1
 
< 0.1%
500000 2
< 0.1%

NHCMP12ANB_1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12ANB_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)33.3%
Missing107116
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0

Common Values

ValueCountFrequency (%)
1.0 3
 
< 0.1%
(Missing) 107116
> 99.9%

Length

2024-04-21T21:46:27.623487image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:27.736420image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3
100.0%

Most occurring characters

ValueCountFrequency (%)
1 3
33.3%
. 3
33.3%
0 3
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6
66.7%
Other Punctuation 3
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3
50.0%
0 3
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3
33.3%
. 3
33.3%
0 3
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3
33.3%
. 3
33.3%
0 3
33.3%

NHCMP12ANB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)2.5%
Missing107079
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
40 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters120
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 40
 
< 0.1%
(Missing) 107079
> 99.9%

Length

2024-04-21T21:46:27.854871image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:27.966488image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 40
100.0%

Most occurring characters

ValueCountFrequency (%)
1 40
33.3%
. 40
33.3%
0 40
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80
66.7%
Other Punctuation 40
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 40
50.0%
0 40
50.0%
Other Punctuation
ValueCountFrequency (%)
. 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 40
33.3%
. 40
33.3%
0 40
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 40
33.3%
. 40
33.3%
0 40
33.3%

NHCMP12ANB_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:28.085894image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:28.192996image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP12AOA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)44.3%
Missing107058
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean1202809.6
Minimum98
Maximum7000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:46:28.307270image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile99
Q1180000
median600000
Q32000000
95-th percentile4000000
Maximum7000000
Range6999902
Interquartile range (IQR)1820000

Descriptive statistics

Standard deviation1456204.5
Coefficient of variation (CV)1.2106692
Kurtosis4.4680329
Mean1202809.6
Median Absolute Deviation (MAD)599901
Skewness1.9000648
Sum73371385
Variance2.1205316 × 1012
MonotonicityNot monotonic
2024-04-21T21:46:28.463643image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
99 13
 
< 0.1%
2000000 6
 
< 0.1%
2500000 5
 
< 0.1%
1000000 4
 
< 0.1%
300000 3
 
< 0.1%
400000 3
 
< 0.1%
1800000 2
 
< 0.1%
250000 2
 
< 0.1%
500000 2
 
< 0.1%
3000000 2
 
< 0.1%
Other values (17) 19
 
< 0.1%
(Missing) 107058
99.9%
ValueCountFrequency (%)
98 1
 
< 0.1%
99 13
< 0.1%
60000 1
 
< 0.1%
180000 1
 
< 0.1%
250000 2
 
< 0.1%
270000 1
 
< 0.1%
300000 3
 
< 0.1%
350000 1
 
< 0.1%
380000 1
 
< 0.1%
400000 3
 
< 0.1%
ValueCountFrequency (%)
7000000 1
 
< 0.1%
6000000 1
 
< 0.1%
4000000 2
 
< 0.1%
3000000 2
 
< 0.1%
2500000 5
< 0.1%
2400000 1
 
< 0.1%
2000000 6
< 0.1%
1800000 2
 
< 0.1%
1500000 1
 
< 0.1%
1300000 1
 
< 0.1%

NHCMP12AOB_1
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:28.611519image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:28.719161image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP12AOB_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)33.3%
Missing107116
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0

Common Values

ValueCountFrequency (%)
1.0 3
 
< 0.1%
(Missing) 107116
> 99.9%

Length

2024-04-21T21:46:28.835092image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:28.945127image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3
100.0%

Most occurring characters

ValueCountFrequency (%)
1 3
33.3%
. 3
33.3%
0 3
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6
66.7%
Other Punctuation 3
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3
50.0%
0 3
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3
33.3%
. 3
33.3%
0 3
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3
33.3%
. 3
33.3%
0 3
33.3%

NHCMP12AOB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.8%
Missing107063
Missing (%)99.9%
Memory size837.0 KiB
1.0
56 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters168
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 56
 
0.1%
(Missing) 107063
99.9%

Length

2024-04-21T21:46:29.062233image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:29.168607image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 56
100.0%

Most occurring characters

ValueCountFrequency (%)
1 56
33.3%
. 56
33.3%
0 56
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 112
66.7%
Other Punctuation 56
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 56
50.0%
0 56
50.0%
Other Punctuation
ValueCountFrequency (%)
. 56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 168
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 56
33.3%
. 56
33.3%
0 56
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 56
33.3%
. 56
33.3%
0 56
33.3%

NHCMP12AOB_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:29.284175image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:29.390293image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP12APA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct51
Distinct (%)37.2%
Missing106982
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean513965.49
Minimum98
Maximum7000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:46:29.520202image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile99
Q140000
median270000
Q3600000
95-th percentile1940000
Maximum7000000
Range6999902
Interquartile range (IQR)560000

Descriptive statistics

Standard deviation903666.27
Coefficient of variation (CV)1.7582236
Kurtosis22.382023
Mean513965.49
Median Absolute Deviation (MAD)250000
Skewness4.1242773
Sum70413272
Variance8.1661273 × 1011
MonotonicityNot monotonic
2024-04-21T21:46:29.699858image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 18
 
< 0.1%
300000 12
 
< 0.1%
1000000 6
 
< 0.1%
98 5
 
< 0.1%
150000 5
 
< 0.1%
20000 5
 
< 0.1%
600000 5
 
< 0.1%
50000 5
 
< 0.1%
500000 5
 
< 0.1%
100000 4
 
< 0.1%
Other values (41) 67
 
0.1%
(Missing) 106982
99.9%
ValueCountFrequency (%)
98 5
 
< 0.1%
99 18
< 0.1%
10000 1
 
< 0.1%
15000 1
 
< 0.1%
18000 1
 
< 0.1%
20000 5
 
< 0.1%
30000 2
 
< 0.1%
40000 2
 
< 0.1%
50000 5
 
< 0.1%
60000 3
 
< 0.1%
ValueCountFrequency (%)
7000000 1
 
< 0.1%
4500000 1
 
< 0.1%
3120000 1
 
< 0.1%
3000000 3
< 0.1%
2500000 1
 
< 0.1%
1800000 1
 
< 0.1%
1500000 2
< 0.1%
1410000 1
 
< 0.1%
1360000 1
 
< 0.1%
1200000 2
< 0.1%

NHCMP12APB_1
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)12.5%
Missing107111
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 8
 
< 0.1%
(Missing) 107111
> 99.9%

Length

2024-04-21T21:46:29.863089image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:29.976878image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 8
100.0%

Most occurring characters

ValueCountFrequency (%)
1 8
33.3%
. 8
33.3%
0 8
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16
66.7%
Other Punctuation 8
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8
50.0%
0 8
50.0%
Other Punctuation
ValueCountFrequency (%)
. 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8
33.3%
. 8
33.3%
0 8
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8
33.3%
. 8
33.3%
0 8
33.3%

NHCMP12APB_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)7.1%
Missing107105
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
14 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters42
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 14
 
< 0.1%
(Missing) 107105
> 99.9%

Length

2024-04-21T21:46:30.101504image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:30.214449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 14
100.0%

Most occurring characters

ValueCountFrequency (%)
1 14
33.3%
. 14
33.3%
0 14
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28
66.7%
Other Punctuation 14
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14
50.0%
0 14
50.0%
Other Punctuation
ValueCountFrequency (%)
. 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 42
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14
33.3%
. 14
33.3%
0 14
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14
33.3%
. 14
33.3%
0 14
33.3%

NHCMP12APB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.9%
Missing107004
Missing (%)99.9%
Memory size837.0 KiB
1.0
115 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters345
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 115
 
0.1%
(Missing) 107004
99.9%

Length

2024-04-21T21:46:30.333927image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:30.443159image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 115
100.0%

Most occurring characters

ValueCountFrequency (%)
1 115
33.3%
. 115
33.3%
0 115
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 230
66.7%
Other Punctuation 115
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 115
50.0%
0 115
50.0%
Other Punctuation
ValueCountFrequency (%)
. 115
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 115
33.3%
. 115
33.3%
0 115
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 115
33.3%
. 115
33.3%
0 115
33.3%

NHCMP12APB_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:30.558324image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:30.668606image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP12AQA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)69.2%
Missing107093
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean312022.81
Minimum98
Maximum1500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:46:30.776491image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile99
Q147500
median155000
Q3500000
95-th percentile950000
Maximum1500000
Range1499902
Interquartile range (IQR)452500

Descriptive statistics

Standard deviation381862.28
Coefficient of variation (CV)1.2238281
Kurtosis2.4160862
Mean312022.81
Median Absolute Deviation (MAD)154901
Skewness1.5843606
Sum8112593
Variance1.458188 × 1011
MonotonicityNot monotonic
2024-04-21T21:46:30.925062image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
99 5
 
< 0.1%
200000 3
 
< 0.1%
500000 2
 
< 0.1%
800000 2
 
< 0.1%
650000 1
 
< 0.1%
1500000 1
 
< 0.1%
400000 1
 
< 0.1%
72000 1
 
< 0.1%
150000 1
 
< 0.1%
80000 1
 
< 0.1%
Other values (8) 8
 
< 0.1%
(Missing) 107093
> 99.9%
ValueCountFrequency (%)
98 1
 
< 0.1%
99 5
< 0.1%
40000 1
 
< 0.1%
70000 1
 
< 0.1%
72000 1
 
< 0.1%
80000 1
 
< 0.1%
90000 1
 
< 0.1%
100000 1
 
< 0.1%
150000 1
 
< 0.1%
160000 1
 
< 0.1%
ValueCountFrequency (%)
1500000 1
 
< 0.1%
1000000 1
 
< 0.1%
800000 2
< 0.1%
650000 1
 
< 0.1%
600000 1
 
< 0.1%
500000 2
< 0.1%
400000 1
 
< 0.1%
200000 3
< 0.1%
160000 1
 
< 0.1%
150000 1
 
< 0.1%

NHCMP12AQB_1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12AQB_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:31.079881image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:31.187773image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP12AQB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)4.0%
Missing107094
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
25 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters75
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 25
 
< 0.1%
(Missing) 107094
> 99.9%

Length

2024-04-21T21:46:31.304776image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:31.412601image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 25
100.0%

Most occurring characters

ValueCountFrequency (%)
1 25
33.3%
. 25
33.3%
0 25
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50
66.7%
Other Punctuation 25
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 25
50.0%
0 25
50.0%
Other Punctuation
ValueCountFrequency (%)
. 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 75
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 25
33.3%
. 25
33.3%
0 25
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 75
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 25
33.3%
. 25
33.3%
0 25
33.3%

NHCMP12AQB_4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12ARA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)36.4%
Missing107075
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean131810.59
Minimum98
Maximum1700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:46:31.522827image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile98
Q199
median99
Q3115000
95-th percentile895000
Maximum1700000
Range1699902
Interquartile range (IQR)114901

Descriptive statistics

Standard deviation330996.66
Coefficient of variation (CV)2.5111537
Kurtosis13.340799
Mean131810.59
Median Absolute Deviation (MAD)1
Skewness3.5650989
Sum5799666
Variance1.0955879 × 1011
MonotonicityNot monotonic
2024-04-21T21:46:31.670826image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
99 20
 
< 0.1%
98 7
 
< 0.1%
250000 3
 
< 0.1%
200000 2
 
< 0.1%
20000 1
 
< 0.1%
110000 1
 
< 0.1%
1080000 1
 
< 0.1%
1700000 1
 
< 0.1%
144000 1
 
< 0.1%
15000 1
 
< 0.1%
Other values (6) 6
 
< 0.1%
(Missing) 107075
> 99.9%
ValueCountFrequency (%)
98 7
 
< 0.1%
99 20
< 0.1%
15000 1
 
< 0.1%
18000 1
 
< 0.1%
20000 1
 
< 0.1%
30000 1
 
< 0.1%
100000 1
 
< 0.1%
110000 1
 
< 0.1%
130000 1
 
< 0.1%
144000 1
 
< 0.1%
ValueCountFrequency (%)
1700000 1
 
< 0.1%
1080000 1
 
< 0.1%
1000000 1
 
< 0.1%
300000 1
 
< 0.1%
250000 3
< 0.1%
200000 2
< 0.1%
144000 1
 
< 0.1%
130000 1
 
< 0.1%
110000 1
 
< 0.1%
100000 1
 
< 0.1%

NHCMP12ARB_1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12ARB_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)16.7%
Missing107113
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 6
 
< 0.1%
(Missing) 107113
> 99.9%

Length

2024-04-21T21:46:31.826112image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:31.938060image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 6
100.0%

Most occurring characters

ValueCountFrequency (%)
1 6
33.3%
. 6
33.3%
0 6
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12
66.7%
Other Punctuation 6
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6
50.0%
0 6
50.0%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6
33.3%
. 6
33.3%
0 6
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6
33.3%
. 6
33.3%
0 6
33.3%

NHCMP12ARB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)2.7%
Missing107082
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
37 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters111
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 37
 
< 0.1%
(Missing) 107082
> 99.9%

Length

2024-04-21T21:46:32.059047image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:32.178756image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 37
100.0%

Most occurring characters

ValueCountFrequency (%)
1 37
33.3%
. 37
33.3%
0 37
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74
66.7%
Other Punctuation 37
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 37
50.0%
0 37
50.0%
Other Punctuation
ValueCountFrequency (%)
. 37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 111
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 37
33.3%
. 37
33.3%
0 37
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 37
33.3%
. 37
33.3%
0 37
33.3%

NHCMP12ARB_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:32.300605image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:32.409570image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMPA12ARA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)29.2%
Missing107095
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean283266.04
Minimum98
Maximum1200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:46:32.511955image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile99
Q199
median99
Q3500000
95-th percentile1000000
Maximum1200000
Range1199902
Interquartile range (IQR)499901

Descriptive statistics

Standard deviation400611.35
Coefficient of variation (CV)1.4142583
Kurtosis0.065240866
Mean283266.04
Median Absolute Deviation (MAD)0
Skewness1.1784616
Sum6798385
Variance1.6048946 × 1011
MonotonicityNot monotonic
2024-04-21T21:46:32.651974image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
99 13
 
< 0.1%
500000 3
 
< 0.1%
1000000 3
 
< 0.1%
300000 2
 
< 0.1%
98 1
 
< 0.1%
497000 1
 
< 0.1%
1200000 1
 
< 0.1%
(Missing) 107095
> 99.9%
ValueCountFrequency (%)
98 1
 
< 0.1%
99 13
< 0.1%
300000 2
 
< 0.1%
497000 1
 
< 0.1%
500000 3
 
< 0.1%
1000000 3
 
< 0.1%
1200000 1
 
< 0.1%
ValueCountFrequency (%)
1200000 1
 
< 0.1%
1000000 3
 
< 0.1%
500000 3
 
< 0.1%
497000 1
 
< 0.1%
300000 2
 
< 0.1%
99 13
< 0.1%
98 1
 
< 0.1%

NHCMPA12ARB_1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMPA12ARB_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing107117
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0

Common Values

ValueCountFrequency (%)
1.0 2
 
< 0.1%
(Missing) 107117
> 99.9%

Length

2024-04-21T21:46:32.793639image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:32.911493image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
66.7%
Other Punctuation 2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2
50.0%
0 2
50.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

NHCMPA12ARB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)4.5%
Missing107097
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
22 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters66
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 22
 
< 0.1%
(Missing) 107097
> 99.9%

Length

2024-04-21T21:46:33.031735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:33.144402image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 22
100.0%

Most occurring characters

ValueCountFrequency (%)
1 22
33.3%
. 22
33.3%
0 22
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44
66.7%
Other Punctuation 22
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 22
50.0%
0 22
50.0%
Other Punctuation
ValueCountFrequency (%)
. 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 66
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 22
33.3%
. 22
33.3%
0 22
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 22
33.3%
. 22
33.3%
0 22
33.3%

NHCMPA12ARB_4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12ASA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)83.3%
Missing107107
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean477500
Minimum25000
Maximum3000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size837.0 KiB
2024-04-21T21:46:33.251703image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum25000
5-th percentile27750
Q148750
median145000
Q3477500
95-th percentile1845000
Maximum3000000
Range2975000
Interquartile range (IQR)428750

Descriptive statistics

Standard deviation838921.11
Coefficient of variation (CV)1.7569029
Kurtosis8.9227645
Mean477500
Median Absolute Deviation (MAD)117500
Skewness2.8890238
Sum5730000
Variance7.0378864 × 1011
MonotonicityNot monotonic
2024-04-21T21:46:33.379331image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
30000 2
 
< 0.1%
90000 2
 
< 0.1%
560000 1
 
< 0.1%
55000 1
 
< 0.1%
450000 1
 
< 0.1%
900000 1
 
< 0.1%
300000 1
 
< 0.1%
3000000 1
 
< 0.1%
200000 1
 
< 0.1%
25000 1
 
< 0.1%
(Missing) 107107
> 99.9%
ValueCountFrequency (%)
25000 1
< 0.1%
30000 2
< 0.1%
55000 1
< 0.1%
90000 2
< 0.1%
200000 1
< 0.1%
300000 1
< 0.1%
450000 1
< 0.1%
560000 1
< 0.1%
900000 1
< 0.1%
3000000 1
< 0.1%
ValueCountFrequency (%)
3000000 1
< 0.1%
900000 1
< 0.1%
560000 1
< 0.1%
450000 1
< 0.1%
300000 1
< 0.1%
200000 1
< 0.1%
90000 2
< 0.1%
55000 1
< 0.1%
30000 2
< 0.1%
25000 1
< 0.1%

NHCMP12ASB_1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12ASB_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:33.519249image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:33.627678image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP12ASB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)10.0%
Missing107109
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
10 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 10
 
< 0.1%
(Missing) 107109
> 99.9%

Length

2024-04-21T21:46:33.740600image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:33.847823image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 10
100.0%

Most occurring characters

ValueCountFrequency (%)
1 10
33.3%
. 10
33.3%
0 10
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20
66.7%
Other Punctuation 10
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10
50.0%
0 10
50.0%
Other Punctuation
ValueCountFrequency (%)
. 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10
33.3%
. 10
33.3%
0 10
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10
33.3%
. 10
33.3%
0 10
33.3%

NHCMP12ASB_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:33.963705image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:34.072585image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP12ATA
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct5
Distinct (%)100.0%
Missing107114
Missing (%)> 99.9%
Memory size837.0 KiB
300000.0
200000.0
250000.0
800000.0
110000.0

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters40
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st row300000.0
2nd row200000.0
3rd row250000.0
4th row800000.0
5th row110000.0

Common Values

ValueCountFrequency (%)
300000.0 1
 
< 0.1%
200000.0 1
 
< 0.1%
250000.0 1
 
< 0.1%
800000.0 1
 
< 0.1%
110000.0 1
 
< 0.1%
(Missing) 107114
> 99.9%

Length

2024-04-21T21:46:34.189463image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:34.312152image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
300000.0 1
20.0%
200000.0 1
20.0%
250000.0 1
20.0%
800000.0 1
20.0%
110000.0 1
20.0%

Most occurring characters

ValueCountFrequency (%)
0 28
70.0%
. 5
 
12.5%
2 2
 
5.0%
1 2
 
5.0%
3 1
 
2.5%
5 1
 
2.5%
8 1
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35
87.5%
Other Punctuation 5
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28
80.0%
2 2
 
5.7%
1 2
 
5.7%
3 1
 
2.9%
5 1
 
2.9%
8 1
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 40
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28
70.0%
. 5
 
12.5%
2 2
 
5.0%
1 2
 
5.0%
3 1
 
2.5%
5 1
 
2.5%
8 1
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28
70.0%
. 5
 
12.5%
2 2
 
5.0%
1 2
 
5.0%
3 1
 
2.5%
5 1
 
2.5%
8 1
 
2.5%

NHCMP12ATB_1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12ATB_2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12ATB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)20.0%
Missing107114
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5
 
< 0.1%
(Missing) 107114
> 99.9%

Length

2024-04-21T21:46:34.454659image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:34.561493image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5
100.0%

Most occurring characters

ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
66.7%
Other Punctuation 5
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5
50.0%
0 5
50.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

NHCMP12ATB_4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12AUA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107084
Missing (%)> 99.9%
Memory size837.0 KiB

NHCMP12AUB_1
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:34.677893image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:34.785591image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP12AUB_2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12AUB_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.3%
Missing107089
Missing (%)> 99.9%
Memory size837.0 KiB
1.0
30 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 30
 
< 0.1%
(Missing) 107089
> 99.9%

Length

2024-04-21T21:46:34.903563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:35.014654image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 30
100.0%

Most occurring characters

ValueCountFrequency (%)
1 30
33.3%
. 30
33.3%
0 30
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60
66.7%
Other Punctuation 30
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 30
50.0%
0 30
50.0%
Other Punctuation
ValueCountFrequency (%)
. 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 30
33.3%
. 30
33.3%
0 30
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 30
33.3%
. 30
33.3%
0 30
33.3%

NHCMP12AUB_4
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)25.0%
Missing107115
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0

Common Values

ValueCountFrequency (%)
1.0 4
 
< 0.1%
(Missing) 107115
> 99.9%

Length

2024-04-21T21:46:35.129787image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:35.243676image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 4
100.0%

Most occurring characters

ValueCountFrequency (%)
1 4
33.3%
. 4
33.3%
0 4
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8
66.7%
Other Punctuation 4
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4
50.0%
0 4
50.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4
33.3%
. 4
33.3%
0 4
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4
33.3%
. 4
33.3%
0 4
33.3%

NHCMP12AUA22
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
50000.0

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row50000.0

Common Values

ValueCountFrequency (%)
50000.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:35.365079image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:35.471002image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
50000.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
0 5
71.4%
5 1
 
14.3%
. 1
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6
85.7%
Other Punctuation 1
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5
83.3%
5 1
 
16.7%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5
71.4%
5 1
 
14.3%
. 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5
71.4%
5 1
 
14.3%
. 1
 
14.3%

NHCMP12AUB22_1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12AUB22_2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12AUB22_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:35.585616image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:35.692729image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP12AUB22_4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12AUA23
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)55.6%
Missing107110
Missing (%)> 99.9%
Memory size837.0 KiB
99.0
200000.0
100000.0
98.0
250000.0

Length

Max length8
Median length8
Mean length6.2222222
Min length4

Characters and Unicode

Total characters56
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)22.2%

Sample

1st row200000.0
2nd row100000.0
3rd row98.0
4th row250000.0
5th row100000.0

Common Values

ValueCountFrequency (%)
99.0 3
 
< 0.1%
200000.0 2
 
< 0.1%
100000.0 2
 
< 0.1%
98.0 1
 
< 0.1%
250000.0 1
 
< 0.1%
(Missing) 107110
> 99.9%

Length

2024-04-21T21:46:35.824022image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:35.977239image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
99.0 3
33.3%
200000.0 2
22.2%
100000.0 2
22.2%
98.0 1
 
11.1%
250000.0 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
0 33
58.9%
. 9
 
16.1%
9 7
 
12.5%
2 3
 
5.4%
1 2
 
3.6%
8 1
 
1.8%
5 1
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 47
83.9%
Other Punctuation 9
 
16.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 33
70.2%
9 7
 
14.9%
2 3
 
6.4%
1 2
 
4.3%
8 1
 
2.1%
5 1
 
2.1%
Other Punctuation
ValueCountFrequency (%)
. 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 56
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 33
58.9%
. 9
 
16.1%
9 7
 
12.5%
2 3
 
5.4%
1 2
 
3.6%
8 1
 
1.8%
5 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33
58.9%
. 9
 
16.1%
9 7
 
12.5%
2 3
 
5.4%
1 2
 
3.6%
8 1
 
1.8%
5 1
 
1.8%

NHCMP12AUB23_1
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing107118
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 107118
> 99.9%

Length

2024-04-21T21:46:36.133531image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:36.243125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
0 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

NHCMP12AUB23_2
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)33.3%
Missing107116
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0

Common Values

ValueCountFrequency (%)
1.0 3
 
< 0.1%
(Missing) 107116
> 99.9%

Length

2024-04-21T21:46:36.359975image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:36.472365image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3
100.0%

Most occurring characters

ValueCountFrequency (%)
1 3
33.3%
. 3
33.3%
0 3
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6
66.7%
Other Punctuation 3
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3
50.0%
0 3
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3
33.3%
. 3
33.3%
0 3
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3
33.3%
. 3
33.3%
0 3
33.3%

NHCMP12AUB23_3
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)20.0%
Missing107114
Missing (%)> 99.9%
Memory size837.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5
 
< 0.1%
(Missing) 107114
> 99.9%

Length

2024-04-21T21:46:36.589784image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:46:36.693945image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5
100.0%

Most occurring characters

ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
66.7%
Other Punctuation 5
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5
50.0%
0 5
50.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

NHCMP12AUB23_4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12AUA24
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12AUB24_1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12AUB24_2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12AUB24_3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

NHCMP12AUB24_4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107119
Missing (%)100.0%
Memory size837.0 KiB

FEX_C
Text

Distinct65203
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Memory size837.0 KiB
2024-04-21T21:46:36.981262image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length16
Median length16
Mean length15.888629
Min length11

Characters and Unicode

Total characters1701974
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50198 ?
Unique (%)46.9%

Sample

1st row47,6106272985279
2nd row51,0122773592785
3rd row45,950197007885
4th row63,0174130393852
5th row69,2046521439094
ValueCountFrequency (%)
1,80463242698892 629
 
0.6%
1,13167701863354 552
 
0.5%
1,4395777439874 232
 
0.2%
1,69849246231156 218
 
0.2%
1,1996481199895 171
 
0.2%
10,7610389527659 125
 
0.1%
29,6745038656597 111
 
0.1%
11,8914002240471 97
 
0.1%
29,4959010692373 89
 
0.1%
26,3450507371557 85
 
0.1%
Other values (65193) 104810
97.8%
2024-04-21T21:46:37.491677image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 178515
10.5%
2 173268
10.2%
3 165597
9.7%
4 164234
9.6%
5 157121
9.2%
6 156849
9.2%
8 154581
9.1%
7 153705
9.0%
9 152442
9.0%
0 138543
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1594855
93.7%
Other Punctuation 107119
 
6.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 178515
11.2%
2 173268
10.9%
3 165597
10.4%
4 164234
10.3%
5 157121
9.9%
6 156849
9.8%
8 154581
9.7%
7 153705
9.6%
9 152442
9.6%
0 138543
8.7%
Other Punctuation
ValueCountFrequency (%)
, 107119
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1701974
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 178515
10.5%
2 173268
10.2%
3 165597
9.7%
4 164234
9.6%
5 157121
9.2%
6 156849
9.2%
8 154581
9.1%
7 153705
9.0%
9 152442
9.0%
0 138543
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1701974
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 178515
10.5%
2 173268
10.2%
3 165597
9.7%
4 164234
9.6%
5 157121
9.2%
6 156849
9.2%
8 154581
9.1%
7 153705
9.0%
9 152442
9.0%
0 138543
8.1%

Interactions

2024-04-21T21:45:29.138713image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:10.645894image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:14.822770image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:19.425413image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:24.602487image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:29.576069image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:41.651638image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:45.353889image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:49.166972image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:53.136526image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:56.775067image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:00.894785image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:05.449225image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:09.314399image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:12.932612image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:16.848081image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:20.926419image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:24.675579image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:27.982708image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:31.552576image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:35.920662image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:39.287829image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:42.641634image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:45.875546image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:49.164164image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:52.524192image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:56.945158image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:00.460541image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:03.920694image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:07.190737image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:10.663870image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:14.259297image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:17.767865image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:22.437031image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:25.806064image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:29.250907image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:10.783324image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:14.945507image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:19.578698image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:24.767363image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:29.735551image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:41.775515image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:45.504996image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:49.288725image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:53.261159image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:56.890374image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:01.045641image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:05.566702image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:09.426245image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:13.057004image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:16.959304image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:21.045221image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:24.805602image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:28.087010image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:31.663819image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:36.041075image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:39.391695image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:42.738235image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:45.973003image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:49.261320image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:52.623766image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:57.045353image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:00.559850image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:04.021749image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:07.290616image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:10.768633image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:14.383650image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:17.874504image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:22.544208image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:25.905894image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:29.362429image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:10.916965image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:15.073579image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:19.733315image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:24.927678image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:29.892079image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:41.900786image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:45.652710image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:49.408942image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:53.386675image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:57.003613image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:01.217597image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:05.692907image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:09.536806image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:13.183295image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:17.076906image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:21.163716image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:24.933082image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:28.188093image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:31.772688image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:36.157112image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:39.496979image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:42.835160image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:46.069924image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:49.360233image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:52.722778image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:57.147320image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:00.659098image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:04.126192image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:07.386551image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:10.879098image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:14.504116image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:17.979499image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:22.650983image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:26.010025image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:29.458063image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:11.044304image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:15.197176image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:19.873333image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:25.085598image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:30.068103image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:42.036775image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:45.794197image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:49.525662image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:53.531610image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:57.131049image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:01.334664image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:05.807760image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:09.641033image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:13.319892image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:17.189002image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:21.280959image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:25.046386image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:28.301626image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:31.889840image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:36.258587image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:39.594977image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:42.932313image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:46.168899image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:49.452367image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:52.823231image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:57.245551image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:00.756082image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:04.223586image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:07.478069image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:10.978464image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:14.605965image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:18.080735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:22.756059image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:26.104456image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:29.550790image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:11.176777image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:15.330849image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:20.028752image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:25.255335image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:30.224218image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:42.147146image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:45.924871image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:49.630986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:53.660985image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-04-21T21:45:03.641794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:06.915258image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:10.383304image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:13.967821image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:17.491380image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:22.157243image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:25.508666image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:28.857026image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:32.159957image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:14.588162image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:19.142842image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:24.298384image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:29.308123image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:41.456453image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:45.151191image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:48.968485image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:52.946915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:56.577870image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:00.621312image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:05.259170image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:09.116884image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:12.736579image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:16.655434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:20.727928image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:24.474057image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:27.807257image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:31.356837image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:35.722089image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:39.098846image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:42.458379image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:45.701719image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:48.981558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:52.330547image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:56.759417image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:00.274058image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:03.738161image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:07.005085image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:10.477490image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:14.074519image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:17.581272image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:22.258264image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:25.620873image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:28.951203image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:32.247385image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:14.695497image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:19.271253image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:24.459101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:29.441549image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:41.563270image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:45.246591image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:49.073170image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:53.047674image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:43:56.683413image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:00.786889image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:05.353998image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:09.215573image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:12.835895image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:16.758653image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:20.816539image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:24.568560image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:27.896606image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:31.459997image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:35.822179image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:39.201172image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:42.555924image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:45.787458image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:49.076810image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:52.423437image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:44:56.849635image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:00.366564image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:03.836410image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:07.102713image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:10.574086image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:14.168470image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:17.678738image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:22.348769image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:25.717318image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:45:29.053422image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-04-21T21:46:37.761965image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
DIRECTORIODIRECTORIO_HOGNHCMP10NHCMP10AAANHCMP10ABANHCMP10ACANHCMP10ADANHCMP10AEANHCMP11ANHCMP11BNHCMP11CNHCMP11DNHCMP11ENHCMP11FNHCMP11GNHCMP11HNHCMP11HBNHCMP11INHCMP11JNHCMP11KNHCMP11LNHCMP11NNHCMP11NUNHCMP11NUBNHCMP11ONHCMP11PNHCMP11QNHCMP11RNHCMP11SNHCMP11SBNHCMP11TNHCMP11UNHCMP12NHCMP12AAANHCMP12ABANHCMP12ACANHCMP12ADANHCMP12AFANHCMP12AGANHCMP12AHANHCMP12AIANHCMP12AJANHCMP12ALANHCMP12ANANHCMP12AOANHCMP12APANHCMP12AQANHCMP12ARANHCMP12ASANHCMP12ATANHCMP12AUA23NHCMP9ANHCMP9ABNHCMP9BNHCMP9BANHCMP9BBNHCMP9CNHCMP9CBNHCMP9DNHCMP9DANHCMP9DBNHCMP9ENHCMPA11ALNHCMPA11RNHCMPA11U24NHCMPA11UB24NHCMPA12ALANHCMPA12ARANHCMPB11U25SECUENCIA_P
DIRECTORIO1.0001.0000.0170.1140.456-0.2250.0000.3460.0340.0240.0240.0290.0250.0040.0250.0190.0650.0250.0220.0210.0990.0560.0230.0520.0620.0350.0200.0290.0090.0300.0280.0270.012-0.117-0.015-0.225-0.2080.500-0.3820.123-0.160-0.0730.084-0.088-0.2000.0260.2260.062-0.6111.0000.0000.105-0.0090.0200.0880.0420.022-0.0090.0190.004-0.0330.0440.0520.0780.093-0.0150.0140.1000.090-0.038
DIRECTORIO_HOG1.0001.0000.0170.1140.456-0.2250.0000.3460.0340.0240.0240.0290.0250.0040.0250.0190.0650.0250.0220.0210.0990.0560.0230.0520.0620.0350.0200.0290.0090.0300.0280.0270.012-0.117-0.015-0.225-0.2080.500-0.3820.123-0.160-0.0730.084-0.088-0.2000.0260.2260.062-0.6111.0000.0000.105-0.0090.0200.0880.0420.022-0.0090.0190.004-0.0330.0440.0520.0780.093-0.0150.0140.1000.090-0.038
NHCMP100.0170.0171.000NaNNaNNaN1.000NaN0.0090.0020.0000.0080.0030.0000.0120.007-0.0010.0060.0000.0020.0230.0040.006-0.0380.0000.0130.0060.0020.0000.0200.0040.0070.2270.2440.2780.0000.0050.4330.4120.1570.2000.281-0.1660.150-0.0250.2130.1550.246-0.0441.0001.0000.0000.0020.004-0.0140.0090.009-0.0250.0000.027-0.0770.0180.0200.0190.0070.008-0.0040.1480.0000.003
NHCMP10AAA0.1140.114NaN1.0000.7160.7121.0001.0000.3320.2390.2350.1150.3990.2810.1620.047-0.0680.1450.1320.0000.1690.3040.5201.0000.2710.0840.2050.1700.079NaN0.0000.0360.0490.3740.0551.000-1.0000.866NaN0.4450.3370.6291.0000.221-0.1450.105-1.000-0.135NaN1.0000.0000.028-0.0160.000-0.316NaN0.0000.0450.000-0.316-0.7750.1470.0000.2160.000NaN0.532NaN1.0000.007
NHCMP10ABA0.4560.456NaN0.7161.000NaN0.000NaN0.0001.0001.0001.0000.0001.0001.0000.000NaN0.6300.0001.0000.3521.0001.000NaN0.8940.4920.2111.0001.000NaN1.0001.0000.0001.0001.000NaNNaNNaNNaN1.000NaNNaNNaNNaNNaNNaNNaNNaNNaN0.0000.0000.7530.0001.000NaNNaN1.000NaN1.000NaNNaN1.0000.0000.7161.000NaNNaNNaN1.000NaN
NHCMP10ACA-0.225-0.225NaN0.712NaN1.000NaNNaN0.4020.3040.0821.0000.4021.0000.0000.000NaN0.0000.2970.0000.0000.0001.000NaN0.0000.0000.0000.0001.000NaN0.0000.0000.0001.000NaNNaNNaNNaNNaNNaN1.000NaNNaNNaNNaNNaNNaNNaNNaN0.0000.0000.000NaN1.000NaNNaN0.480NaN1.000NaNNaN0.2320.0000.0001.000NaN1.000NaN1.000NaN
NHCMP10ADA0.0000.0001.0001.0000.000NaN1.000NaN1.0001.0001.0000.7911.0001.0000.7911.000NaN1.0001.0000.7911.0001.0001.000NaN1.0000.5231.0001.0000.000NaN1.0001.0000.292NaNNaNNaNNaNNaNNaNNaN1.0001.000NaNNaNNaNNaNNaNNaNNaN0.0000.0000.250NaN1.000NaNNaN1.000NaN0.000NaNNaN0.7910.0000.7911.000NaNNaNNaN1.000NaN
NHCMP10AEA0.3460.346NaN1.000NaNNaNNaN1.0000.0000.0001.0000.0001.0001.0000.0000.000NaN0.0000.0000.0000.0000.0001.000NaN0.0000.0810.0000.0001.000NaN0.5740.0000.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0000.0000.000-1.0001.000NaNNaN1.000NaN1.000NaNNaN0.0000.0000.4061.000NaNNaNNaN0.000NaN
NHCMP11A0.0340.0340.0090.3320.0000.4021.0000.0001.0000.3170.1010.1940.1180.1410.1700.150-0.0830.3000.2700.1950.1370.2200.167-0.0540.2140.0720.1140.1030.1080.0390.0780.1510.012-0.166-0.142-0.105-0.324-0.433-0.5300.0130.045-0.1520.092-0.252-0.065-0.0780.000-0.378-0.6391.0001.0000.261-0.0520.064-0.056-0.0380.169-0.0030.035-0.0530.0050.2120.0390.1480.0070.0180.075-0.1760.0000.008
NHCMP11B0.0240.0240.0020.2391.0000.3041.0000.0000.3171.0000.1030.1820.1220.1330.1790.130-0.0690.2390.2460.1960.1440.2030.165-0.0590.1980.0750.1030.1130.0830.0010.0820.1560.010-0.151-0.0020.288-0.030NaN-0.530-0.1140.064-0.0010.0840.0360.174-0.0200.266-0.131-0.6391.0001.0000.235-0.0420.060-0.0550.0220.1580.0190.040-0.0940.0660.2070.0560.1600.001-0.0800.129-0.3480.0000.005
NHCMP11C0.0240.0240.0000.2351.0000.0821.0001.0000.1010.1031.0000.1430.0680.0790.1880.100-0.0320.1380.1350.1540.1020.1140.1080.0070.1050.0710.0700.1210.0470.1020.1140.1970.0040.059-0.083NaN-0.187NaN-0.236-0.2490.029-0.141NaN-0.064-0.119-0.152-0.135NaN0.0651.0001.0000.134-0.0230.117-0.0530.0130.1380.0080.0630.049-0.0350.1620.0940.1140.036-0.036-0.093NaN0.0270.009
NHCMP11D0.0290.0290.0080.1151.0001.0000.7910.0000.1940.1820.1431.0000.1210.1190.1780.132-0.0690.1770.1740.2090.1370.1860.181-0.0120.1750.0790.0930.1210.0780.0030.1040.1580.004-0.239-0.303-0.432-0.222-0.722NaNNaN-0.134-0.170-0.0660.034-0.053-0.099-0.0670.072-0.3691.0000.0000.182-0.0600.068-0.0710.0100.185-0.0090.049-0.0160.0220.2150.0740.1490.000-0.009-0.025-0.3780.0000.011
NHCMP11E0.0250.0250.0030.3990.0000.4021.0001.0000.1180.1220.0680.1211.0000.0830.1390.089-0.0520.1120.1050.1190.1020.1330.114-0.0540.1060.0500.0610.0730.0560.0040.0620.0840.000-0.1100.0560.288-0.043-0.612NaN0.127NaN-0.161NaNNaN-0.188-0.078NaNNaN-0.2191.0001.0000.110-0.0280.047-0.009-0.0230.095-0.0300.0290.009-0.0090.1270.0340.1270.000-0.0350.001-0.3780.0000.003
NHCMP11F0.0040.0040.0000.2811.0001.0001.0001.0000.1410.1330.0790.1190.0831.0000.1040.094-0.0490.1530.1550.1200.0640.1600.133-0.0080.1490.0290.0740.0630.107-0.0360.0540.0810.009-0.018-0.119-0.432-0.395-0.612-0.412NaN-0.155-0.018NaN-0.247-0.234NaNNaN-0.259NaN1.0001.0000.136-0.0390.054-0.061-0.0080.116-0.0180.0320.0230.0380.1190.0180.0750.0000.0200.044NaN0.000-0.003
NHCMP11G0.0250.0250.0120.1621.0000.0000.7910.0000.1700.1790.1880.1780.1390.1041.0000.171-0.0800.1610.1510.2070.3740.2000.170-0.0580.1720.1100.0800.1690.078-0.0610.1420.2160.001-0.182-0.2270.000-0.1700.000-0.1160.1880.176-0.0510.1500.108-0.1420.1270.3310.012-0.2241.0000.7560.200-0.0520.092-0.102-0.0110.176-0.0160.058-0.0290.0190.2750.1320.4230.041-0.023-0.1140.2140.0460.015
NHCMP11H0.0190.0190.0070.0470.0000.0001.0000.0000.1500.1300.1000.1320.0890.0940.1711.000NaN0.1900.1460.1520.0970.1300.129-0.0420.1050.0380.0890.0940.049-0.0580.0550.1010.013-0.063-0.133-0.211-0.128-0.4080.412-0.117-0.044-0.0410.1310.255-0.2370.0120.4250.0300.3341.0001.0000.191-0.0340.060-0.015-0.0100.106-0.0060.0350.0030.0430.1440.0100.1150.0030.0030.093-0.0990.0000.001
NHCMP11HB0.0650.065-0.001-0.068NaNNaNNaNNaN-0.083-0.069-0.032-0.069-0.052-0.049-0.080NaN1.0000.0000.0000.0410.0400.0630.0570.5360.0540.0300.0000.0110.0000.6570.0490.0270.000NaNNaNNaNNaNNaNNaNNaN0.707NaNNaNNaNNaNNaNNaNNaNNaN0.0000.0000.0450.4530.000-0.0540.5040.0720.4160.0630.0870.2560.0690.0280.0550.0001.0001.000NaN0.000-0.020
NHCMP11I0.0250.0250.0060.1450.6300.0001.0000.0000.3000.2390.1380.1770.1120.1530.1610.1900.0001.0000.3420.1980.0930.1860.160-0.0710.1710.0580.1180.1070.093-0.0150.0840.1520.017-0.067-0.072NaN-0.406-0.4080.412-0.1760.0010.0260.319-0.0690.2410.0210.5030.0190.2701.0001.0000.236-0.0480.088-0.049-0.0120.1500.0030.053-0.048-0.0020.1880.0360.1090.000-0.0540.0170.3180.0000.004
NHCMP11J0.0220.0220.0000.1320.0000.2971.0000.0000.2700.2460.1350.1740.1050.1550.1510.1460.0000.3421.0000.1850.0880.1860.175-0.0320.1660.0510.1070.0960.099-0.0290.0870.1510.015-0.179-0.1260.086-0.361-0.408NaN-0.074-0.011-0.0760.2300.0120.122-0.0860.4250.0300.5871.0001.0000.219-0.0470.0880.023-0.0090.167-0.0140.0490.0000.0050.1710.0360.1000.000-0.1190.0110.2140.0000.005
NHCMP11K0.0210.0210.0020.0001.0000.0000.7910.0000.1950.1960.1540.2090.1190.1200.2070.1520.0410.1980.1851.0000.1040.1800.156-0.0350.1600.0870.1240.1350.072-0.0470.0880.1740.004-0.139-0.172-0.374-0.320-0.791NaNNaN-0.172-0.0990.038-0.285-0.123-0.033NaN-0.135-0.6431.0001.0000.214-0.0420.093-0.035-0.0130.142-0.0150.059-0.0190.0340.2060.0450.1250.020-0.0220.031NaN0.014-0.001
NHCMP11L0.0990.0990.0230.1690.3520.0001.0000.0000.1370.1440.1020.1370.1020.0640.3740.0970.0400.0930.0880.1041.0000.1920.106-0.0650.1900.1400.0360.1430.050-0.0780.1260.1270.022-0.072-0.0830.000-0.118-0.4740.1330.0000.186-0.0340.182-0.259-0.382-0.0280.379-0.140-0.4761.0001.0000.187-0.0480.028-0.183-0.0400.158-0.0180.023-0.069-0.0400.3070.3280.7650.022-0.022-0.079-0.0620.0350.033
NHCMP11N0.0560.0560.0040.3041.0000.0001.0000.0000.2200.2030.1140.1860.1330.1600.2000.1300.0630.1860.1860.1800.1921.0000.214-0.0250.5250.0760.2340.1370.117-0.1210.1070.1510.005-0.160-0.105NaN-0.150NaNNaNNaN-0.154-0.165-0.0140.1610.058NaN0.121-0.112-0.4821.0001.0000.235-0.0560.052-0.099-0.0080.198-0.0230.035-0.0140.0280.2440.0720.2070.000-0.0400.095-0.2140.0000.004
NHCMP11NU0.0230.0230.0060.5201.0001.0001.0001.0000.1670.1650.1080.1810.1140.1330.1700.1290.0570.1600.1750.1560.1060.2141.000NaN0.1880.0570.1060.1030.116-0.0290.1080.1500.004-0.228-0.207-0.432-0.370-0.7910.059NaN-0.109-0.173-0.0920.1570.158NaNNaN0.041-0.1311.0001.0000.148-0.0490.061-0.049-0.0090.178-0.0230.038-0.0190.0030.1630.0460.1200.0080.025-0.074-0.0480.0230.004
NHCMP11NUB0.0520.052-0.0381.000NaNNaNNaNNaN-0.054-0.0590.007-0.012-0.054-0.008-0.058-0.0420.536-0.071-0.032-0.035-0.065-0.025NaN1.0000.0540.0000.0170.0330.0040.5370.0000.0520.098NaNNaNNaNNaNNaNNaNNaNNaN0.000NaNNaNNaNNaNNaNNaNNaN0.0000.0000.0280.3880.000-0.1370.4400.0150.4620.000-0.1360.2370.0610.0000.0570.000NaNNaNNaN0.3450.026
NHCMP11O0.0620.0620.0000.2710.8940.0001.0000.0000.2140.1980.1050.1750.1060.1490.1720.1050.0540.1710.1660.1600.1900.5250.1880.0541.0000.0680.1450.1230.101-0.0980.1170.1320.001-0.098-0.165-0.432-0.317NaNNaN-0.161-0.154-0.132-0.144-0.065-0.146NaNNaN-0.187-0.4821.0001.0000.227-0.0720.048-0.120-0.0430.208-0.0220.034-0.032-0.0280.2450.0750.2040.0000.025-0.054-0.3090.0180.007
NHCMP11P0.0350.0350.0130.0840.4920.0000.5230.0810.0720.0750.0710.0790.0500.0290.1100.0380.0300.0580.0510.0870.1400.0760.0570.0000.0681.0000.0380.1120.0300.0050.0360.1030.000-0.122-0.1930.357-0.270NaN-0.656-0.144-0.120-0.060-0.015-0.260-0.139-0.038-0.2030.0040.0251.0000.0000.102-0.0070.038-0.0150.0150.0620.0050.029-0.0580.0030.1250.1270.1580.0780.045-0.007-0.0420.0540.029
NHCMP11Q0.0200.0200.0060.2050.2110.0001.0000.0000.1140.1030.0700.0930.0610.0740.0800.0890.0000.1180.1070.1240.0360.2340.1060.0170.1450.0381.0000.0730.068-0.0300.0280.0890.016-0.132-0.316NaN-0.086-0.316-0.133-0.054-0.175-0.112-0.0690.123-0.148-0.1430.000-0.235NaN1.0001.0000.138-0.0110.048-0.002-0.0110.0880.0050.038-0.0430.0150.1130.0150.0460.0000.027-0.207NaN0.000-0.008
NHCMP11R0.0290.0290.0020.1701.0000.0001.0000.0000.1030.1130.1210.1210.0730.0630.1690.0940.0110.1070.0960.1350.1430.1370.1030.0330.1230.1120.0731.0000.038-0.0090.1020.1830.002-0.121-0.032NaN0.049NaN-0.540-0.159-0.039-0.285-0.3360.142-0.089-0.1800.097-0.045-0.2601.0001.0000.125-0.0470.064-0.0340.0560.122-0.0110.048-0.0060.0330.2070.1820.1780.000-0.018-0.129-0.3810.0000.017
NHCMP11S0.0090.0090.0000.0791.0001.0000.0001.0000.1080.0830.0470.0780.0560.1070.0780.0490.0000.0930.0990.0720.0500.1170.1160.0040.1010.0300.0680.0381.000NaN0.0440.0690.000-0.110NaNNaN-0.367NaN0.059NaNNaN-0.161-0.264NaNNaNNaNNaNNaN-0.0441.0001.0000.072-0.0420.033-0.028-0.0130.106-0.0130.0250.0430.0270.0790.0160.0550.0000.012-0.169NaN0.0000.000
NHCMP11SB0.0300.0300.020NaNNaNNaNNaNNaN0.0390.0010.1020.0030.004-0.036-0.061-0.0580.657-0.015-0.029-0.047-0.078-0.121-0.0290.537-0.0980.005-0.030-0.009NaN1.0000.0960.0000.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0000.0000.0700.2930.000-0.2610.7660.0000.6220.000NaNNaN0.0000.0000.0330.000NaNNaNNaN0.000-0.025
NHCMP11T0.0280.0280.0040.0001.0000.0001.0000.5740.0780.0820.1140.1040.0620.0540.1420.0550.0490.0840.0870.0880.1260.1070.1080.0000.1170.0360.0280.1020.0440.0961.0000.1430.003-0.093-0.097NaN-0.238-0.6120.059NaNNaNNaN-0.264-0.065-0.032NaNNaN-0.177NaN1.0001.0000.083-0.0480.044-0.053-0.0160.121-0.0180.034-0.026-0.0760.1420.1420.1370.0050.020-0.088NaN0.0060.012
NHCMP11U0.0270.0270.0070.0361.0000.0001.0000.0000.1510.1560.1970.1580.0840.0810.2160.1010.0270.1520.1510.1740.1270.1510.1500.0520.1320.1030.0890.1830.0690.0000.1431.0000.010-0.167-0.183-0.036-0.355-0.408-0.059-0.1360.136-0.1020.167-0.172-0.247-0.158NaN-0.2460.1401.0001.0000.153-0.0220.089-0.0740.0450.1430.0180.068-0.0210.0550.1960.2100.1560.0420.010-0.062NaN0.0570.017
NHCMP120.0120.0120.2270.0490.0000.0000.2920.0000.0120.0100.0040.0040.0000.0090.0010.0130.0000.0170.0150.0040.0220.0050.0040.0980.0010.0000.0160.0020.0000.0000.0030.0101.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0001.0000.0040.0050.0000.0080.0000.0020.0100.007-0.0020.0220.0000.0210.0180.0070.012NaNNaN0.000-0.001
NHCMP12AAA-0.117-0.1170.2440.3741.0001.000NaNNaN-0.166-0.1510.059-0.239-0.110-0.018-0.182-0.063NaN-0.067-0.179-0.139-0.072-0.160-0.228NaN-0.098-0.122-0.132-0.121-0.110NaN-0.093-0.167NaN1.0000.7251.0000.751-1.000NaN0.6750.7770.9181.0000.5740.9731.0001.000-1.000NaNNaN0.0000.252-0.0460.4611.000NaN0.353NaN0.5350.400NaN0.2680.2430.0001.000NaN0.8751.0000.6500.058
NHCMP12ABA-0.015-0.0150.2780.0551.000NaNNaNNaN-0.142-0.002-0.083-0.3030.056-0.119-0.227-0.133NaN-0.072-0.126-0.172-0.083-0.105-0.207NaN-0.165-0.193-0.316-0.032NaNNaN-0.097-0.183NaN0.7251.0001.0000.4120.500NaN0.8800.8420.682NaN0.4670.9220.7870.5001.000NaN1.0000.0000.0000.0130.5120.671-0.3540.199NaN0.0000.058NaN0.1660.1670.1341.000NaN0.806NaN1.0000.066
NHCMP12ACA-0.225-0.2250.0001.000NaNNaNNaNNaN-0.1050.288NaN-0.4320.288-0.4320.000-0.211NaNNaN0.086-0.3740.000NaN-0.432NaN-0.4320.357NaNNaNNaNNaNNaN-0.036NaN1.0001.0001.000NaNNaNNaNNaN1.0001.000NaNNaNNaNNaNNaNNaNNaN0.0001.0000.000NaN0.964NaNNaN0.600NaN0.000-0.500NaN0.6000.0000.0001.000NaN1.000NaN1.000NaN
NHCMP12ADA-0.208-0.2080.005-1.000NaNNaNNaNNaN-0.324-0.030-0.187-0.222-0.043-0.395-0.170-0.128NaN-0.406-0.361-0.320-0.118-0.150-0.370NaN-0.317-0.270-0.0860.049-0.367NaN-0.238-0.355NaN0.7510.412NaN1.000NaNNaNNaN0.6910.8291.000NaN1.000NaNNaNNaNNaN0.0000.0000.588-0.0560.508-0.051NaN0.000NaN1.000NaNNaN0.3270.0000.4851.000NaN1.0001.0001.000NaN
NHCMP12AFA0.5000.5000.4330.866NaNNaNNaNNaN-0.433NaNNaN-0.722-0.612-0.6120.000-0.408NaN-0.408-0.408-0.791-0.474NaN-0.791NaNNaNNaN-0.316NaNNaNNaN-0.612-0.408NaN-1.0000.500NaNNaN1.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0000.0000.000-0.7751.000NaNNaN1.000NaN0.000NaNNaN0.8940.8940.0001.000NaNNaNNaN1.000NaN
NHCMP12AGA-0.382-0.3820.412NaNNaNNaNNaNNaN-0.530-0.530-0.236NaNNaN-0.412-0.1160.412NaN0.412NaNNaN0.133NaN0.059NaNNaN-0.656-0.133-0.5400.059NaN0.059-0.059NaNNaNNaNNaNNaNNaN1.000NaNNaNNaN0.778NaNNaNNaNNaN1.000NaN0.0000.0000.177NaN0.707NaNNaN0.707NaN1.000NaNNaN0.7070.0000.3851.000NaN1.0001.0001.000NaN
NHCMP12AHA0.1230.1230.1570.4451.000NaNNaNNaN0.013-0.114-0.249NaN0.127NaN0.188-0.117NaN-0.176-0.074NaN0.000NaNNaNNaN-0.161-0.144-0.054-0.159NaNNaNNaN-0.136NaN0.6750.880NaNNaNNaNNaN1.0000.4030.400NaN1.000NaN0.000NaNNaNNaN0.0000.0000.053NaN1.000NaNNaN0.000NaN1.000NaNNaN0.6840.0000.0991.000NaNNaNNaN1.000NaN
NHCMP12AIA-0.160-0.1600.2000.337NaN1.0001.000NaN0.0450.0640.029-0.134NaN-0.1550.176-0.0440.7070.001-0.011-0.1720.186-0.154-0.109NaN-0.154-0.120-0.175-0.039NaNNaNNaN0.136NaN0.7770.8421.0000.691NaNNaN0.4031.0000.8211.0000.2721.0000.5261.000NaNNaNNaN0.0000.2700.0510.3670.949-0.2580.000NaN0.0001.000NaN0.0000.1620.0001.000NaN0.000NaN1.000-0.052
NHCMP12AJA-0.073-0.0730.2810.629NaNNaN1.000NaN-0.152-0.001-0.141-0.170-0.161-0.018-0.051-0.041NaN0.026-0.076-0.099-0.034-0.165-0.1730.000-0.132-0.060-0.112-0.285-0.161NaNNaN-0.102NaN0.9180.6821.0000.829NaNNaN0.4000.8211.0001.0000.4471.0000.8161.000NaN1.0001.0000.0000.1420.0940.5880.975-0.3540.284NaN0.000-0.500NaN0.1990.1310.0001.000NaN1.0001.0001.000NaN
NHCMP12ALA0.0840.084-0.1661.000NaNNaNNaNNaN0.0920.084NaN-0.066NaNNaN0.1500.131NaN0.3190.2300.0380.182-0.014-0.092NaN-0.144-0.015-0.069-0.336-0.264NaN-0.2640.167NaN1.000NaNNaN1.000NaN0.778NaN1.0001.0001.000NaNNaNNaNNaN1.000NaN0.0000.0000.0000.5320.564NaNNaN0.000NaN1.000NaNNaN0.0000.0000.3111.000NaN0.9090.9331.000NaN
NHCMP12ANA-0.088-0.0880.1500.221NaNNaNNaNNaN-0.2520.036-0.0640.034NaN-0.2470.1080.255NaN-0.0690.012-0.285-0.2590.1610.157NaN-0.065-0.2600.1230.142NaNNaN-0.065-0.172NaN0.5740.467NaNNaNNaNNaN1.0000.2720.447NaN1.0000.741-1.0000.569NaNNaNNaN0.0000.0000.3820.0001.000NaN0.0000.7750.000NaNNaN0.0000.0000.3241.000NaNNaNNaN1.000NaN
NHCMP12AOA-0.200-0.200-0.025-0.145NaNNaNNaNNaN-0.0650.174-0.119-0.053-0.188-0.234-0.142-0.237NaN0.2410.122-0.123-0.3820.0580.158NaN-0.146-0.139-0.148-0.089NaNNaN-0.032-0.247NaN0.9730.922NaN1.000NaNNaNNaN1.0001.000NaN0.7411.000NaN1.000NaNNaN0.0000.0000.000NaN0.0001.000NaN0.134NaN0.000NaNNaN0.0000.0000.4191.000NaN0.920NaN1.000NaN
NHCMP12APA0.0260.0260.2130.105NaNNaNNaNNaN-0.078-0.020-0.152-0.099-0.078NaN0.1270.012NaN0.021-0.086-0.033-0.028NaNNaNNaNNaN-0.038-0.143-0.180NaNNaNNaN-0.158NaN1.0000.787NaNNaNNaNNaN0.0000.5260.816NaN-1.000NaN1.000NaNNaNNaN0.0001.0000.0360.1000.0000.000NaN0.000-1.0001.000NaNNaN0.0000.1300.0000.000NaN0.8731.0001.000NaN
NHCMP12AQA0.2260.2260.155-1.000NaNNaNNaNNaN0.0000.266-0.135-0.067NaNNaN0.3310.425NaN0.5030.425NaN0.3790.121NaNNaNNaN-0.2030.0000.097NaNNaNNaNNaNNaN1.0000.500NaNNaNNaNNaNNaN1.0001.000NaN0.5691.000NaN1.000NaNNaNNaN0.0000.050NaN0.0001.000NaN1.000NaN1.000NaNNaN0.0000.3010.0001.000NaNNaNNaN1.000NaN
NHCMP12ARA0.0620.0620.246-0.135NaNNaNNaNNaN-0.378-0.131NaN0.072NaN-0.2590.0120.030NaN0.0190.030-0.135-0.140-0.1120.041NaN-0.1870.004-0.235-0.045NaNNaN-0.177-0.246NaN-1.0001.000NaNNaNNaN1.000NaNNaNNaN1.000NaNNaNNaNNaN1.000NaN0.0000.0000.314NaN0.000NaNNaN0.000NaN1.000NaNNaN0.2230.0000.3581.000NaN1.0001.0001.0000.177
NHCMP12ASA-0.611-0.611-0.044NaNNaNNaNNaNNaN-0.639-0.6390.065-0.369-0.219NaN-0.2240.334NaN0.2700.587-0.643-0.476-0.482-0.131NaN-0.4820.025NaN-0.260-0.044NaNNaN0.140NaNNaNNaNNaNNaNNaNNaNNaNNaN1.000NaNNaNNaNNaNNaNNaN1.0000.0000.0000.214NaN1.000NaNNaN0.447NaN1.000NaNNaN0.8940.2580.3571.000NaNNaNNaN1.000NaN
NHCMP12ATA1.0001.0001.0001.0000.0000.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0000.0001.0001.0001.000NaN1.0000.0000.0000.0000.0000.000NaN1.0000.000NaN0.0000.000NaN0.0000.0001.0000.0001.000NaN1.000NaNNaN1.000NaN1.000NaNNaN1.0001.0001.0000.000NaN1.000NaN0.000NaN
NHCMP12AUA230.0000.0001.0000.0000.0000.0000.0000.0001.0001.0001.0000.0001.0001.0000.7561.0000.0001.0001.0001.0001.0001.0001.0000.0001.0000.0001.0001.0001.0000.0001.0001.0001.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0001.0001.000NaN1.000NaNNaN1.000NaN1.000NaNNaN0.7560.0001.0001.000NaNNaNNaN1.000NaN
NHCMP9A0.1050.1050.0000.0280.7530.0000.2500.0000.2610.2350.1340.1820.1100.1360.2000.1910.0450.2360.2190.2140.1870.2350.1480.0280.2270.1020.1380.1250.0720.0700.0830.1530.0040.2520.0000.0000.5880.0000.1770.0530.2700.1420.0000.0000.0000.0360.0500.3140.2141.0001.0001.000NaN0.081-0.0210.0130.202-0.0130.053-0.0170.0350.2920.0900.2000.0250.0280.175-0.1290.0150.003
NHCMP9AB-0.009-0.0090.002-0.0160.000NaNNaN-1.000-0.052-0.042-0.023-0.060-0.028-0.039-0.052-0.0340.453-0.048-0.047-0.042-0.048-0.056-0.0490.388-0.072-0.007-0.011-0.047-0.0420.293-0.048-0.0220.005-0.0460.013NaN-0.056-0.775NaNNaN0.0510.0940.5320.382NaN0.100NaNNaNNaNNaNNaNNaN1.0000.016-0.0150.4470.0430.4860.000-0.0780.2670.0850.0170.0650.0000.2590.1140.2270.000-0.009
NHCMP9B0.0200.0200.0040.0001.0001.0001.0001.0000.0640.0600.1170.0680.0470.0540.0920.0600.0000.0880.0880.0930.0280.0520.0610.0000.0480.0380.0480.0640.0330.0000.0440.0890.0000.4610.5120.9640.5081.0000.7071.0000.3670.5880.5640.0000.0000.0000.0000.0001.0001.0001.0000.0810.0161.000NaNNaN0.084-0.0170.0570.0530.0430.0790.0230.0370.0110.037-0.0920.1480.0190.003
NHCMP9BA0.0880.088-0.014-0.316NaNNaNNaNNaN-0.056-0.055-0.053-0.071-0.009-0.061-0.102-0.015-0.054-0.0490.023-0.035-0.183-0.099-0.049-0.137-0.120-0.015-0.002-0.034-0.028-0.261-0.053-0.0740.0081.0000.671NaN-0.051NaNNaNNaN0.9490.975NaN1.0001.0000.0001.000NaNNaNNaNNaN-0.021-0.015NaN1.000-0.1660.000-0.1040.0000.366-0.0720.0370.0270.0280.000NaNNaNNaN0.000-0.041
NHCMP9BB0.0420.0420.009NaNNaNNaNNaNNaN-0.0380.0220.0130.010-0.023-0.008-0.011-0.0100.504-0.012-0.009-0.013-0.040-0.008-0.0090.440-0.0430.015-0.0110.056-0.0130.766-0.0160.0450.000NaN-0.354NaNNaNNaNNaNNaN-0.258-0.354NaNNaNNaNNaNNaNNaNNaNNaNNaN0.0130.447NaN-0.1661.0000.0000.6250.000NaNNaN0.0000.0000.0450.000NaNNaNNaN0.0000.013
NHCMP9C0.0220.0220.0090.0001.0000.4801.0001.0000.1690.1580.1380.1850.0950.1160.1760.1060.0720.1500.1670.1420.1580.1980.1780.0150.2080.0620.0880.1220.1060.0000.1210.1430.0020.3530.1990.6000.0001.0000.7070.0000.0000.2840.0000.0000.1340.0001.0000.0000.4471.0001.0000.2020.0430.0840.0000.0001.000NaN0.0430.035-0.0130.2380.0940.1640.0170.0060.028-0.1210.0090.009
NHCMP9CB-0.009-0.009-0.0250.045NaNNaNNaNNaN-0.0030.0190.008-0.009-0.030-0.018-0.016-0.0060.4160.003-0.014-0.015-0.018-0.023-0.0230.462-0.0220.0050.005-0.011-0.0130.622-0.0180.0180.010NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.775NaN-1.000NaNNaNNaNNaNNaN-0.0130.486-0.017-0.1040.625NaN1.0000.080-0.1370.5710.0350.0000.0060.0000.694-0.351NaN0.000-0.015
NHCMP9D0.0190.0190.0000.0001.0001.0000.0001.0000.0350.0400.0630.0490.0290.0320.0580.0350.0630.0530.0490.0590.0230.0350.0380.0000.0340.0290.0380.0480.0250.0000.0340.0680.0070.5350.0000.0001.0000.0001.0001.0000.0000.0001.0000.0000.0001.0001.0001.0001.0001.0001.0000.0530.0000.0570.0000.0000.0430.0801.000NaNNaN0.0610.0260.0300.0120.023NaN-0.3780.0110.001
NHCMP9DA0.0040.0040.027-0.316NaNNaNNaNNaN-0.053-0.0940.049-0.0160.0090.023-0.0290.0030.087-0.0480.000-0.019-0.069-0.014-0.019-0.136-0.032-0.058-0.043-0.0060.043NaN-0.026-0.021-0.0020.4000.058-0.500NaNNaNNaNNaN1.000-0.500NaNNaNNaNNaNNaNNaNNaNNaNNaN-0.017-0.0780.0530.366NaN0.035-0.137NaN1.000-0.1180.0100.0000.0491.000NaNNaNNaN1.0000.037
NHCMP9DB-0.033-0.033-0.077-0.775NaNNaNNaNNaN0.0050.066-0.0350.022-0.0090.0380.0190.0430.256-0.0020.0050.034-0.0400.0280.0030.237-0.0280.0030.0150.0330.027NaN-0.0760.0550.022NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0350.2670.043-0.072NaN-0.0130.571NaN-0.1181.0000.0000.0000.0000.000NaNNaNNaN0.000-0.013
NHCMP9E0.0440.0440.0180.1471.0000.2320.7910.0000.2120.2070.1620.2150.1270.1190.2750.1440.0690.1880.1710.2060.3070.2440.1630.0610.2450.1250.1130.2070.0790.0000.1420.1960.0000.2680.1660.6000.3270.8940.7070.6840.0000.1990.0000.0000.0000.0000.0000.2230.8941.0000.7560.2920.0850.0790.0370.0000.2380.0350.0610.0100.0001.0000.1750.3290.000-0.065-0.100-0.2970.0150.014
NHCMPA11AL0.0520.0520.0200.0000.0000.0000.0000.0000.0390.0560.0940.0740.0340.0180.1320.0100.0280.0360.0360.0450.3280.0720.0460.0000.0750.1270.0150.1820.0160.0000.1420.2100.0210.2430.1670.0000.0000.8940.0000.0000.1620.1310.0000.0000.0000.1300.3010.0000.2581.0000.0000.0900.0170.0230.0270.0000.0940.0000.0260.0000.0000.1751.0000.2790.0430.020-0.014-0.4210.0640.044
NHCMPA11R0.0780.0780.0190.2160.7160.0000.7910.4060.1480.1600.1140.1490.1270.0750.4230.1150.0550.1090.1000.1250.7650.2070.1200.0570.2040.1580.0460.1780.0550.0330.1370.1560.0180.0000.1340.0000.4850.0000.3850.0990.0000.0000.3110.3240.4190.0000.0000.3580.3571.0001.0000.2000.0650.0370.0280.0450.1640.0060.0300.0490.0000.3290.2791.0000.025-0.013-0.180-0.3480.0380.031
NHCMPA11U240.0930.0930.0070.0001.0001.0001.0001.0000.0070.0010.0360.0000.0000.0000.0410.0030.0000.0000.0000.0200.0220.0000.0080.0000.0000.0780.0000.0000.0000.0000.0050.0420.0071.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0000.0001.0000.0250.0000.0110.0000.0000.0170.0000.0121.0000.0000.0000.0430.0251.000NaNNaNNaN0.289-0.003
NHCMPA11UB24-0.015-0.0150.008NaNNaNNaNNaNNaN0.018-0.080-0.036-0.009-0.0350.020-0.0230.0031.000-0.054-0.119-0.022-0.022-0.0400.025NaN0.0250.0450.027-0.0180.012NaN0.0200.0100.012NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0280.2590.037NaNNaN0.0060.6940.023NaNNaN-0.0650.020-0.013NaN1.000NaNNaN0.000-0.016
NHCMPA12ALA0.0140.014-0.0040.532NaN1.000NaNNaN0.0750.129-0.093-0.0250.0010.044-0.1140.0931.0000.0170.0110.031-0.0790.095-0.074NaN-0.054-0.007-0.207-0.129-0.169NaN-0.088-0.062NaN0.8750.8061.0001.000NaN1.000NaN0.0001.0000.909NaN0.9200.873NaN1.000NaN1.000NaN0.1750.114-0.092NaNNaN0.028-0.351NaNNaNNaN-0.100-0.014-0.180NaNNaN1.0000.9351.000NaN
NHCMPA12ARA0.1000.1000.148NaNNaNNaNNaNNaN-0.176-0.348NaN-0.378-0.378NaN0.214-0.099NaN0.3180.214NaN-0.062-0.214-0.048NaN-0.309-0.042NaN-0.381NaNNaNNaNNaNNaN1.000NaNNaN1.000NaN1.000NaNNaN1.0000.933NaNNaN1.000NaN1.000NaNNaNNaN-0.1290.2270.148NaNNaN-0.121NaN-0.378NaNNaN-0.297-0.421-0.348NaNNaN0.9351.0001.000NaN
NHCMPB11U250.0900.0900.0001.0001.0001.0001.0000.0000.0000.0000.0270.0000.0000.0000.0460.0000.0000.0000.0000.0140.0350.0000.0230.3450.0180.0540.0000.0000.0000.0000.0060.0570.0000.6501.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0000.0150.0000.0190.0000.0000.0090.0000.0111.0000.0000.0150.0640.0380.2890.0001.0001.0001.000-0.007
SECUENCIA_P-0.038-0.0380.0030.007NaNNaNNaNNaN0.0080.0050.0090.0110.003-0.0030.0150.001-0.0200.0040.005-0.0010.0330.0040.0040.0260.0070.029-0.0080.0170.000-0.0250.0120.017-0.0010.0580.066NaNNaNNaNNaNNaN-0.052NaNNaNNaNNaNNaNNaN0.177NaNNaNNaN0.003-0.0090.003-0.0410.0130.009-0.0150.0010.037-0.0130.0140.0440.031-0.003-0.016NaNNaN-0.0071.000

Missing values

2024-04-21T21:45:33.216909image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T21:45:36.684934image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DIRECTORIODIRECTORIO_HOGSECUENCIA_PNHCMP9ANHCMP9AANHCMP9ABNHCMP9BNHCMP9BANHCMP9BBNHCMP9CNHCMP9CANHCMP9CBNHCMP9DNHCMP9DANHCMP9DBNHCMP9ENHCMP9EANHCMP10NHCMP10A_1NHCMP10A_2NHCMP10A_3NHCMP10A_4NHCMP10A_5NHCMP10AAANHCMP10AAB_1NHCMP10AAB_2NHCMP10AAB_3NHCMP10AAB_4NHCMP10ABANHCMP10ABB_1NHCMP10ABB_2NHCMP10ABB_3NHCMP10ABB_4NHCMP10ACANHCMP10ACB_1NHCMP10ACB_2NHCMP10ACB_3NHCMP10ACB_4NHCMP10ADANHCMP10ADB_1NHCMP10ADB_2NHCMP10ADB_3NHCMP10ADB_4NHCMP10AEANHCMP10AEB_1NHCMP10AEB_2NHCMP10AEB_3NHCMP10AEB_4NHCMP11ANHCMP11AANHCMP11ABNHCMP11BNHCMP11BANHCMP11BBNHCMP11CNHCMP11CANHCMP11CBNHCMP11DNHCMP11DANHCMP11DBNHCMP11ENHCMP11EANHCMP11EBNHCMP11FNHCMP11FANHCMP11FBNHCMP11GNHCMP11GANHCMP11GBNHCMP11HNHCMP11HANHCMP11HBNHCMP11INHCMP11IANHCMP11IBNHCMP11JNHCMP11JANHCMP11JBNHCMP11KNHCMP11KANHCMP11KBNHCMPA11ALNHCMPA11ALANHCMPA11ALBNHCMP11LNHCMP11LANHCMP11LBNHCMP11NNHCMP11NANHCMP11NBNHCMP11ONHCMP11OANHCMP11OBNHCMP11PNHCMP11PANHCMP11PBNHCMP11QNHCMP11QANHCMP11QBNHCMP11RNHCMP11RANHCMP11RBNHCMPA11RNHCMPA11RANHCMPA11RBNHCMP11SNHCMP11SANHCMP11SBNHCMP11TNHCMP11TANHCMP11TBNHCMP11UNHCMP11UANHCMP11UBNHCMP11NUNHCMP11NUANHCMP11NUBNHCMPA11U24NHCMPA11UA24NHCMPA11UB24NHCMPB11U25NHCMPB11UA25NHCMPB11UB25NHCMP12NHCMP12A_1NHCMP12A_2NHCMP12A_3NHCMP12A_4NHCMP12A_5NHCMP12A_6NHCMP12A_7NHCMP12A_8NHCMP12A_9NHCMP12A_10NHCMP12A_11NHCMP12A_12NHCMP12A_13NHCMP12A_14NHCMP12A_15NHCMP12A_16NHCMP12A_17NHCMP12A_18NHCMP12A_19NHCMP12A_20NHCMP12A_21NHCMP12A_22NHCMP12A_23NHCMP12A_24NHCMP12A_25NHCMP12AAANHCMP12AAB_1NHCMP12AAB_2NHCMP12AAB_3NHCMP12AAB_4NHCMP12ABANHCMP12ABB_1NHCMP12ABB_2NHCMP12ABB_3NHCMP12ABB_4NHCMP12ACANHCMP12ACB_1NHCMP12ACB_2NHCMP12ACB_3NHCMP12ACB_4NHCMP12ADANHCMP12ADB_1NHCMP12ADB_2NHCMP12ADB_3NHCMP12ADB_4NHCMP12AEANHCMP12AEB_1NHCMP12AEB_2NHCMP12AEB_3NHCMP12AEB_4NHCMP12AFANHCMP12AFB_1NHCMP12AFB_2NHCMP12AFB_3NHCMP12AFB_4NHCMP12AGANHCMP12AGB_1NHCMP12AGB_2NHCMP12AGB_3NHCMP12AGB_4NHCMP12AHANHCMP12AHB_1NHCMP12AHB_2NHCMP12AHB_3NHCMP12AHB_4NHCMP12AIANHCMP12AIB_1NHCMP12AIB_2NHCMP12AIB_3NHCMP12AIB_4NHCMP12AJANHCMP12AJB_1NHCMP12AJB_2NHCMP12AJB_3NHCMP12AJB_4NHCMP12AKANHCMP12AKB_1NHCMP12AKB_2NHCMP12AKB_3NHCMP12AKB_4NHCMPA12ALANHCMPA12ALB_1NHCMPA12ALB_2NHCMPA12ALB_3NHCMPA12ALB_4NHCMP12ALANHCMP12ALB_1NHCMP12ALB_2NHCMP12ALB_3NHCMP12ALB_4NHCMP12ANANHCMP12ANB_1NHCMP12ANB_2NHCMP12ANB_3NHCMP12ANB_4NHCMP12AOANHCMP12AOB_1NHCMP12AOB_2NHCMP12AOB_3NHCMP12AOB_4NHCMP12APANHCMP12APB_1NHCMP12APB_2NHCMP12APB_3NHCMP12APB_4NHCMP12AQANHCMP12AQB_1NHCMP12AQB_2NHCMP12AQB_3NHCMP12AQB_4NHCMP12ARANHCMP12ARB_1NHCMP12ARB_2NHCMP12ARB_3NHCMP12ARB_4NHCMPA12ARANHCMPA12ARB_1NHCMPA12ARB_2NHCMPA12ARB_3NHCMPA12ARB_4NHCMP12ASANHCMP12ASB_1NHCMP12ASB_2NHCMP12ASB_3NHCMP12ASB_4NHCMP12ATANHCMP12ATB_1NHCMP12ATB_2NHCMP12ATB_3NHCMP12ATB_4NHCMP12AUANHCMP12AUB_1NHCMP12AUB_2NHCMP12AUB_3NHCMP12AUB_4NHCMP12AUA22NHCMP12AUB22_1NHCMP12AUB22_2NHCMP12AUB22_3NHCMP12AUB22_4NHCMP12AUA23NHCMP12AUB23_1NHCMP12AUB23_2NHCMP12AUB23_3NHCMP12AUB23_4NHCMP12AUA24NHCMP12AUB24_1NHCMP12AUB24_2NHCMP12AUB24_3NHCMP12AUB24_4FEX_C
01165355.01165355111200000.00.02NaNNaN2NaNNaN2NaNNaN2NaN2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN11e+060.02NaNNaN2NaNNaN13e+050.02NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaNNaNNaNNaNNaNNaNNaN2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN47,6106272985279
11165356.01165356112NaNNaN2NaNNaN130000.00.02NaNNaN160000.02NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN13500000.02NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaNNaNNaNNaNNaNNaNNaN2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN51,0122773592785
21165357.01165357112NaNNaN2NaNNaN1100000.00.02NaNNaN2NaN2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN1990.02NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaNNaNNaNNaNNaNNaNNaN2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN45,950197007885
31165358.01165358112NaNNaN2NaNNaN1100000.00.02NaNNaN180000.02NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN111000000.02NaNNaN2NaNNaN2NaNNaN2NaNNaN1250000.00.014500000.02NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaNNaNNaNNaNNaNNaNNaN2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN63,0174130393852
41165359.01165359111200000.00.02NaNNaN2NaNNaN2NaNNaN2NaN2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN12e+0702NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN2NaNNaN118000000.015e+050.013000000.00.014e+060.02NaNNaN2NaNNaN12e+050.012500000.00.02NaNNaN2NaNNaN2NaNNaN2NaNNaNNaNNaNNaNNaNNaNNaN2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN69,2046521439094
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